JP4905723B2 - Image processing system, image processing method, and program - Google Patents

Description

  The present invention relates to an image processing system, an image processing method, and a program. The present invention particularly relates to an image processing system and an image processing method for processing an image, and a program for the image processing system.

2. Description of the Related Art A face image extraction apparatus that acquires a face image is known which extracts a person's face from an input image whose frame rate is converted by thinning an input video in a time direction to convert the frame rate. (For example, refer to Patent Document 1). In this apparatus, the reproduction of the range in which the highest evaluation value of the face image can be obtained is instructed. Also, there is known an image encoding device that encodes information for specifying the ROI region, explicitly includes the encoded image and the encoded information, and generates an encoded stream (for example, , See Patent Document 2).
JP 2006-303897 A JP 2006-129249 A

  As described in the above-mentioned patent document, if a frame is thinned and an ROI region such as a face region is specified, information on the ROI region of the frame lost by thinning cannot be left. For this reason, if the ROI area is missed, it may not be possible to specify the exact ROI area later.

  In order to solve the above-described problem, according to a first aspect of the present invention, an image processing system includes an image acquisition unit that acquires a plurality of captured images captured at different timings, and a plurality of captured images. Information indicating a feature region detection unit that detects a feature region, an image selection unit that selects some captured images from a plurality of captured images, and a region that includes a plurality of feature regions detected from the plurality of captured images Are output in association with some captured images.

  According to the second aspect of the present invention, there is provided an image processing method, an image acquisition stage for acquiring a plurality of captured images captured at different timings, and a feature region detection for detecting a feature region from each of the plurality of captured images. Information indicating a stage, an image selection stage for selecting a part of the picked-up images from the plurality of picked-up images, and a region including a plurality of feature areas detected from the plurality of picked-up images. And an output stage for outputting in association with each other.

  According to the third aspect of the present invention, there is provided a program for an image processing device, wherein the computer acquires an image acquisition unit that acquires a plurality of captured images captured at different timings, and a feature region from each of the plurality of captured images. A feature region detection unit to detect, an image selection unit to select a part of the picked-up image from a plurality of picked-up images, a part of information indicating a region including a plurality of feature regions detected from the plurality of picked-up images It functions as an output unit that outputs images in association with images.

  It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

  FIG. 1 shows an example of an image processing system 10 according to an embodiment. An object of the image processing system 10 is to reduce the amount of code of an image while maintaining the image quality of a characteristic area in the image. The image processing system 10 can function as a monitoring system as described below.

  The image processing system 10 includes a plurality of imaging devices 100a to 100d (hereinafter collectively referred to as imaging devices 100) that capture the monitoring target space 150, and a plurality of image processing devices 120a that process captured images captured by the imaging device 100. The image processing apparatus 120b (hereinafter collectively referred to as the image processing apparatus 120), the communication network 110, the image processing apparatus 170, the image DB 175, and the plurality of display apparatuses 180a-d (hereinafter collectively referred to as the display apparatus 180). Prepare.

  The image processing device 120a is connected to the imaging device 100a and the imaging device 100b, and the image processing device 120b is connected to the imaging device 100c and the imaging device 100d. Note that the image processing device 170 and the display device 180 are provided in a space 160 different from the monitoring target space 150.

  The imaging device 100a includes an imaging unit 102a and an imaging moving image compression unit 104a. The imaging unit 102a captures a plurality of captured images by capturing the monitoring target space 150 continuously at different timings. The imaging unit 102a generates a captured moving image including a plurality of captured images. The captured moving image compression unit 104a compresses the captured moving image captured by the imaging unit 102a by MPEG encoding or the like, and generates captured moving image data. Thus, the imaging device 100a encodes the captured moving image obtained by capturing the monitoring target space 150 to generate captured moving image data. The imaging device 100a outputs the captured moving image data to the image processing device 120a to which the imaging device 100a is connected.

  Note that the imaging device 100b, the imaging device 100c, and the imaging device 100d have the same configuration as that of the imaging device 100a, and thus description of each component of the imaging device 100b, the imaging device 100c, and the imaging device 100d is omitted. Note that the imaging device 100b outputs the generated captured moving image data to the image processing device 120a to which the imaging device 100b is connected. In addition, the imaging device 100c and the imaging device 100d output the generated captured moving image data to the image processing device 120b. In this way, the image processing device 120 acquires the captured moving image data generated by the imaging device 100 from the imaging device 100 to which each is connected.

  Then, the image processing device 120 decodes the captured moving image data acquired from the imaging device 100 and acquires the captured moving image. The image processing apparatus 120 has different types of features such as an area where a person 130 is imaged, an area where a moving body 140 such as a vehicle is imaged, etc., from each of a plurality of captured images included in the acquired captured video. A plurality of feature regions are detected. Then, the image processing device 120 selects a captured image to be transmitted to the image processing device 170, and generates compressed moving image data in which the selected captured image is compressed.

  Further, the image processing apparatus 120 includes information specifying a feature region detected from a captured image selected as a captured image to be transmitted and a feature region detected from a captured image not selected as the captured image to be transmitted. And feature region information including information for specifying the. Then, the image processing apparatus 120 attaches the feature area information to the compressed moving image data and transmits the feature area information to the image processing apparatus 170 through the communication network 110.

  The image processing device 170 receives the compressed moving image data associated with the feature area information from the image processing device 120. Then, the image processing device 170 expands the received compressed moving image data using the associated feature area information to generate a display moving image, and supplies the generated display moving image to the display device 180. The display device 180 displays the moving image for display supplied from the image processing device 170.

  The image processing apparatus 170 may record the compressed moving image data in the image DB 175 in association with the feature area information associated with the compressed moving image data. Then, in response to a request from the display device 180, the image processing device 170 reads the compressed moving image data and the feature area information from the image DB 175, decompresses the read compressed moving image data using the feature region information, and displays it. A moving image may be generated and supplied to the display device 180.

  The feature area information is text data including the position of the feature area, the size of the feature area, the number of feature areas, identification information for identifying the captured image in which the feature area is detected, or the like, or compressed or encrypted into the text data. It may be data that has been processed. Then, the image processing apparatus 170 identifies captured images that satisfy various search conditions based on the position of the feature region, the size of the feature region, the number of feature regions, and the like included in the feature region information. Then, the image processing device 170 may decode the identified captured image and provide the decoded captured image to the display device 180.

  As described above, according to the image processing system 10, since the characteristic region is recorded in association with the moving image, it is possible to quickly search and find a captured image that matches a predetermined condition in the moving image. Further, according to the image processing system 10, only a captured image that meets a predetermined condition can be decoded, so that a partial moving image that meets the predetermined condition can be displayed promptly in response to a reproduction instruction.

  Further, according to the image processing system 10, the compressed moving image data is generated by thinning out the captured image, so that the data amount of the compressed moving image data transmitted to the communication network 110 can be reduced. On the other hand, according to the image processing system 10, the feature region information detected from the thinned captured image is also attached to the compressed moving image data and transmitted to the image processing device 170. Can be detected again more precisely, or the image of the feature region can be analyzed in more detail.

  FIG. 2 shows an example of a block configuration of the image processing apparatus 120. The image processing apparatus 120 includes an image acquisition unit 250 and an image processing unit 200. The image acquisition unit 250 includes a compressed moving image acquisition unit 201 and a compressed moving image expansion unit 202.

  The compressed moving image acquisition unit 201 acquires a compressed moving image. Specifically, the compressed moving image acquisition unit 201 acquires encoded captured moving image data generated by the imaging device 100. The compressed moving image expansion unit 202 expands the captured moving image data acquired by the compressed moving image acquisition unit 201 and generates a plurality of captured images included in the captured moving image. Specifically, the compressed moving image decompression unit 202 decodes the encoded captured moving image data acquired by the compressed moving image acquisition unit 201, and generates a plurality of captured images included in the captured moving image. The captured image included in the captured moving image may be a frame image and a field image. As described above, the image acquisition unit 250 acquires a plurality of captured images captured at different timings.

  The image processing unit 200 detects a feature region from a captured moving image including a plurality of captured images generated by the compressed moving image decompression unit 202, performs compression processing on the captured moving image according to the feature of the detected feature region, The image is output to the image processing apparatus 170. Hereinafter, an example of the operation of the image processing unit 200 will be described.

  The image processing unit 200 includes a feature region detection unit 203, a compression control unit 210, an image selection unit 220, an importance level calculation unit 240, a compression unit 230, an association processing unit 206, and an output unit 207. The feature region detection unit 203 detects a feature region from a moving image including a plurality of captured images. Specifically, the feature region detection unit 203 detects a feature region from each of the plurality of captured images. Note that the captured moving image described above may be an example of a moving image in the following description.

  For example, the feature region detection unit 203 may detect a region including an object indicating a moving object as a feature region. Note that the feature region detection unit 203 may detect a plurality of feature regions having different types of features from each of the plurality of captured images. Note that the type of feature may be an index of the type of object such as a person and a moving object. The type of the object may be determined based on the degree of coincidence of the shape of the object or the color of the object. As described above, the feature region detection unit 203 may detect a plurality of feature regions having different types of included objects from a plurality of captured images.

  For example, the feature region detection unit 203 extracts an object that matches a predetermined shape pattern with a matching degree equal to or higher than a predetermined matching degree from each of the plurality of picked-up images, and in the picked-up image including the extracted object. The region may be detected as a feature region having the same feature type. Note that a plurality of shape patterns may be determined for each type of feature. As an example of the shape pattern, a shape pattern of a human face can be exemplified. Different face patterns may be determined for each of a plurality of persons. Thereby, the feature region detection unit 203 can detect different regions each including a different person as different feature regions.

  The feature region detection unit 203 supplies information indicating the detected feature region to the image selection unit 220, the compression control unit 210, and the importance level calculation unit 240. Note that the information indicating the feature region includes the coordinate information of the feature region indicating the position of the feature region, the type information indicating the type of the feature region, the information identifying the captured moving image in which the feature region is detected, and the detection reliability of the feature region. Including degrees.

  The image selection unit 220 selects some captured images from a plurality of captured images. Specifically, the image selection unit 220 selects some captured images to be transmitted to the image processing apparatus 170. For example, the image selection unit 220 may select some captured images from a plurality of captured images according to the imaging time of the captured image. For example, the image selection unit 220 may select some captured images captured at predetermined time intervals.

  In addition, the image selection unit 220 may select a part of the captured images that meet a predetermined condition from a plurality of captured images. In addition, the image selection unit 220 may select some captured images in which the image content of the feature area detected by the feature area detection unit 203 meets a predetermined condition. In addition, the image selection unit 220 may select some captured images in which the number of feature regions detected by the feature region detection unit 203 is greater than a predetermined value.

  The importance calculation unit 240 calculates the importance for each of a plurality of image areas in a part of the captured images based on the detection reliability of each of the plurality of feature areas detected by the feature area detection unit 203. The importance calculation method by the importance calculation unit 240 will be described later. The importance calculation unit 240 supplies information indicating the calculated importance of each image region to the compression control unit 210.

  Based on the information indicating the feature area acquired from the feature area detection unit 203 and the information indicating the importance level acquired from the importance level calculation unit 240, the compression control unit 210 performs a video compression process by the compression unit 230 according to the feature area. To control. The compression unit 230 is selected by the image selection unit 220 by reducing the image quality of a region other than the feature region detected by the feature region detection unit 203 or a region other than a region whose importance is greater than a predetermined value. Compress some captured images. Note that the compression unit 230 generates a compressed moving image including a plurality of compressed captured images. A more specific compression operation of the compression unit 230 will be described later.

  The association processing unit 206 associates information for identifying the feature area detected from the captured image other than the partially captured image with the partially captured image. Specifically, the association processing unit 206 associates information for specifying a feature area detected from captured images other than some captured images with a compressed moving image including some captured images as moving image constituent images. Note that the association processing unit 206 captures information for specifying a feature region detected from a captured image other than a part of the captured image and information for specifying a feature region detected from a part of the captured image. It may be associated with an image.

  Note that the information specifying the feature region may include information indicating the position of the feature region detected from the captured image other than some captured images. Further, the information specifying the feature region may include information indicating the timing at which captured images other than some captured images are captured. In addition, the information specifying the feature region may include information indicating the size (for example, area) of the feature region detected from the captured image other than some captured images.

  The output unit 207 outputs information specifying a feature region detected from captured images other than some of the captured images associated by the association processing unit 206 to the image processing apparatus 170. As described above, the output unit 207 captures a part of information that specifies a feature region detected from a captured image other than a part of the captured image and information that specifies a feature region detected from the part of the captured image. Output in association with the image. For example, the output unit 207 includes information indicating the position of a feature region detected from a captured image other than some captured images, information indicating the timing at which captured images other than some captured images are captured, or some Information indicating the size of the feature region detected from the captured image other than the captured image can be output in association with a part of the captured images.

  Another example of the association operation by the association processing unit 206 will be described below. The association processing unit 206 associates information indicating a region including a plurality of feature regions detected from a plurality of captured images with a part of the captured images. Specifically, the association processing unit 206 associates information indicating a region including a plurality of feature regions detected from a plurality of captured images with a compressed moving image including a part of the captured images as a moving image constituent image. Note that the association processing unit 206 may associate information indicating a region including at least a part of a plurality of feature regions detected from a plurality of captured images with a part of the captured images.

  Note that the information indicating a region including a plurality of feature regions detected from a plurality of captured images may include information indicating a region including a plurality of feature regions detected from a plurality of captured images. Further, the information indicating the area including the plurality of feature areas detected from the plurality of captured images may include information indicating the set area of the plurality of feature areas detected from the plurality of captured images. Further, the information indicating a region including a plurality of feature regions detected from a plurality of captured images may include information indicating an image region whose importance calculated by the importance calculation unit 240 is larger than a predetermined value.

  Note that the importance level calculation unit 240 has a plurality of image regions in a set region of a plurality of feature regions detected from a plurality of captured images based on the detection reliability of the plurality of feature regions detected by the feature region detection unit 203. The importance is calculated every time. For example, the importance calculation unit 240 calculates an addition value obtained by adding the detection reliability of the feature area including the image area for each of the plurality of image areas in the collection area, and a plurality of values in the collection area are calculated based on the addition value. Importance is calculated for each image area. For example, the importance calculation unit 240 may calculate a greater importance as the added value is larger. Further, the importance calculation unit 240 may calculate the importance determined in advance according to the added value for each of a plurality of image areas.

  In this manner, the output unit 207 outputs information indicating a region including a plurality of feature regions detected from a plurality of captured images in association with a part of the captured images. More specifically, the output unit 207 includes information indicating a region including a plurality of feature regions detected from a plurality of captured images, information indicating a set region of a plurality of feature regions detected from the plurality of captured images, Alternatively, the information indicating the collection area and the importance calculated by the importance calculation unit 240 can be output in association with some captured images.

  Note that the association processing unit 206 may associate information indicating the feature type of the feature region detected from the captured image other than the partially captured image with the partially captured image. Further, the association processing unit 206 may associate information indicating the type of the object included in the feature region detected from the captured image other than the partially captured image with the partially captured image. In this way, the output unit 207 outputs information indicating the type of feature in the feature area detected from the captured image other than the part of the captured image, or the feature area detected from the captured image other than the part of the captured image. Information indicating the types of objects included can be output in association with some captured images.

  FIG. 3 shows an example of a block configuration of the compression unit 230. The compression unit 230 includes an image dividing unit 232, a plurality of fixed value conversion units 234a-c (hereinafter may be collectively referred to as a fixed value conversion unit 234), and a plurality of compression processing units 236a-d (hereinafter referred to as compression processing). Part 236 in some cases). As will be described below, the compression unit 230 compresses the feature region and the region other than the feature region with different image quality.

  Note that the “plurality of captured images” in the description of FIGS. 3, 4, and 6 are captured images supplied from the image selection unit 220 to the compression unit 230 as captured moving images to be compressed by the compression unit 230. Good. That is, “a plurality of captured images” in the following description of this figure may be a part of the captured images selected by the image selection unit 220. In addition, the “feature region” in the following description is a feature region detected from a part of the captured image selected by the image selection unit 220, and a feature region detected from a captured image other than the part of the captured image. It may be a region including For example, the “feature region” in the following description may be the inclusion region or the collective region described above.

  The image dividing unit 232 divides a plurality of captured images into a feature region and a background region other than the feature region. More specifically, the image dividing unit 232 divides a plurality of captured images into each of a plurality of feature areas and a background area other than the feature areas. As described above, the image dividing unit 232 divides each of the plurality of captured images into the feature region and the background region. Then, the compression processing unit 236 compresses the feature region image, which is the feature region image, and the background region image, which is the background region image, with different strengths. Specifically, the compression processing unit 236 compresses a feature area moving image including a plurality of feature area images and a background area moving image including a plurality of background area images with different strengths.

  Specifically, the image dividing unit 232 generates a feature area moving image for each of a plurality of feature types by dividing a plurality of captured images. Then, the fixed value unit 234 fixes the pixel values of the regions other than the feature regions of the respective feature types for each of the feature region images included in the plurality of feature region moving images generated for each feature type. To do. Specifically, the fixed value converting unit 234 sets pixel values in regions other than the feature region to predetermined pixel values. Then, the compression processing unit 236 compresses a plurality of feature area moving images for each feature type. For example, the compression processing unit 236 performs MPEG compression on a plurality of feature area moving images for each feature type.

  The fixed value unit 234a, the fixed value unit 234b, and the fixed value unit 234c are a feature region moving image of the first feature type, a feature region moving image of the second feature type, and a third feature type, respectively. The feature area video of is fixed. Then, the compression processing unit 236a, the compression processing unit 236b, and the compression processing unit 236c are the feature region moving image of the first feature type, the feature region moving image of the second feature type, and the feature of the third feature type. Compress area video.

  Note that the compression processing units 236a-c compress the feature area moving image with a predetermined strength according to the type of feature. For example, the compression processing unit 236 may convert the feature area moving image to a different resolution determined in advance according to the type of the feature, and compress the converted feature area moving image. In addition, when compressing the feature region moving image by MPEG encoding, the compression processing unit 236 may compress the feature region moving image with different quantization parameters determined in advance according to the feature type.

  The compression processing unit 236d compresses the background area moving image. Note that the compression processing unit 236d may compress the background region moving image with a strength higher than the strength of any of the compression processing units 236a-c. The feature area moving image and the background area moving image compressed by the compression processing unit 236 are supplied to the association processing unit 206.

  Since regions other than the feature region have been fixed values by the fixed value unit 234, when the compression processing unit 236 performs predictive encoding by MPEG encoding or the like, the region other than the feature region may be connected to the predicted image. The amount of difference between the images can be significantly reduced. Therefore, the compression rate of the feature area moving image can be significantly increased.

  FIG. 4 shows an example of a block configuration of the image processing apparatus 170. The image processing apparatus 170 includes a compressed image acquisition unit 301, a correspondence analysis unit 302, a decompression control unit 310, a decompression unit 320, a composition unit 330, a feature region processing unit 350, and an output unit 340. The compressed image acquisition unit 301 acquires a compressed moving image including the captured image compressed by the compression unit 230. Specifically, the compressed image acquisition unit 301 acquires a compressed moving image including a plurality of feature area moving images and a background area moving image. More specifically, the compressed image acquisition unit 301 acquires a compressed moving image with feature area information attached thereto.

  Then, the association analysis unit 302 separates the plurality of feature area videos and background area videos and feature area information, and supplies the plurality of feature area videos and background area videos to the decompression unit 320. The association analysis unit 302 also analyzes the feature region information and supplies the feature region position and the feature type to the extension control unit 310 and the feature region processing unit 350. The decompression control unit 310 controls the decompression processing by the decompression unit 320 according to the position of the feature region and the feature type acquired from the association analysis unit 302. For example, the decompression control unit 310 decompresses each region of the moving image indicated by the compressed moving image to the decompressing unit 320 according to the compression method in which the compression unit 230 compresses each region of the moving image according to the position of the feature region and the type of feature. Let

  Hereinafter, the operation of each component included in the extension unit 320 will be described. The decompression unit 320 includes decoders 322a-d (hereinafter collectively referred to as decoders 322), a synthesis unit 330, and an output unit 340. The decoder 322 decodes one of the plurality of encoded characteristic area moving images and background area moving images. Specifically, the decoder 322a, the decoder 322b, the decoder 322c, and the decoder 322d decode the first feature region moving image, the second feature region moving image, the third feature region moving image, and the background region moving image, respectively.

  The synthesizing unit 330 synthesizes a plurality of feature area moving images and background area moving images expanded by the expanding unit 320 to generate one display moving image. Specifically, the combining unit 330 generates one display moving image by combining the image of the feature region on the captured image included in the plurality of feature region moving images with the captured image included in the background region moving image. The display moving image generated by the combining unit 330 is supplied to the feature region processing unit 350.

  The feature region processing unit 350 analyzes image processing related to the feature region or an image of the feature region in the display moving image. The display moving image processed by the feature area processing unit 350 is supplied to the output unit 340. The output unit 340 outputs the feature area information and the display moving image acquired from the association analysis unit 302 to the display device 180 or the image DB 175. The image DB 175 associates the position of the feature region indicated by the feature region information, the feature type of the feature region, and the number of feature regions with the information for identifying the captured image included in the display moving image, and the non-volatile memory such as a hard disk. May be recorded on the recording medium.

  FIG. 5 shows an example of a block configuration of the feature area processing unit 350. The feature region processing unit 350 includes a feature region image selection unit 352, a feature region specification unit 354, a reliability calculation unit 356, a relevance calculation unit 358, and an image processing unit 360.

  The feature area image selection unit 352 acquires a plurality of captured images included in the display moving image supplied from the synthesis unit 330. As described above, the feature region image selection unit 352 selects a part of the captured image output by associating the information indicating the feature region detected from the captured image other than the part of the captured image with the feature region. Choose as. In this manner, the feature region image selection unit 352 selects a captured image in which a feature region is detected from captured images other than some captured images. In the subsequent processing, the display moving image is processed using the information of the feature areas.

  The feature region specifying unit 354, the reliability calculation unit 356, and the relevance calculation unit 358 acquire the captured image selected by the feature region image selection unit 352 and the feature region information supplied from the association analysis unit 302. The feature area specifying unit 354 is based on information indicating the position of the feature area detected from the captured image other than a part of the captured images output in association with the captured image selected by the feature area image selection unit 352. A feature region is specified from the part of the captured image. For example, the feature region specifying unit 354 may specify the feature region in the part of the captured image from the region near the position of the feature region detected from the captured image other than the part of the captured image.

  As described above, the feature region specifying unit 354 is based on the information indicating the position of the feature region detected from the captured image other than the part of the captured image output in association with the part of the captured image from the output unit 207. The feature region can be specified from the part of the captured images. As a result, the image processing apparatus 170 can re-detect the feature region with higher accuracy.

  Note that the feature region specifying unit 354 may acquire information indicating the collection region from the association analysis unit 302 and re-detect the feature region from within the collection region in some captured images. As described above, the feature region specifying unit 354 can specify the feature region from the part of the captured images based on the information indicating the collection region output in association with the part of the captured images. As described above, the image processing apparatus 170 can specify a feature region also from an image region indicated by a feature region detected from a region other than some captured images. Therefore, according to the image processing system 10, it is possible to narrow the specific target region for specifying the feature region in the image processing device 170 while reducing the probability of failing to detect the feature region from some captured images. For this reason, the image processing apparatus 170 can specify the feature region at high speed and with high accuracy.

  The reliability calculation unit 356 is based on information indicating the position of the feature region detected from the captured image other than the part of the captured image output in association with the captured image selected by the feature region image selection unit 352. The detection reliability of the feature region in some captured images is calculated. For example, the reliability calculation unit 356 has a feature area in a part of the captured image in which the position difference between the position of the feature area detected from the captured image other than the part of the captured image is smaller than a predetermined value. The detection reliability may be calculated higher.

  For example, the reliability calculation unit 356 detects a part detected at a position having a correlation higher than a predetermined value with a feature region detected from a captured image other than a part of the captured image. The detection reliability of the feature region in the captured image may be calculated higher. As described above, the reliability calculation unit 356 is based on the information indicating the position of the feature region detected from the captured image other than the partial captured image output from the output unit 207 in association with the partial captured image. The detection reliability of the feature region in a part of the captured images can be calculated. Thereby, the image processing apparatus 170 can reconfirm the probability of the feature region detected in a part of the captured images.

  The relevance calculation unit 358 is based on information indicating the position of the feature region detected from the captured image other than the part of the captured image output in association with the captured image selected by the feature region image selection unit 352. Relevance of feature regions detected from different captured images is calculated. For example, the relevance calculation unit 358 calculates the relevance of a higher feature region when the continuity of the position of the feature region output in association with a part of different captured images is higher. In addition, the relevance calculation unit 358 determines in advance the degree of coincidence of the moving directions of the feature areas indicated by the positions of the feature areas output in association with the different partial captured images. If it is greater than the given value, the relevance of the higher feature area may be calculated.

  As described above, the relevance calculation unit 358 is based on the information indicating the position of the feature region detected from the captured image other than the part of the captured image output in association with the part of the captured image from the output unit 207. Then, the relevance of the feature areas detected from different part of the captured images is calculated. For this reason, the image processing apparatus 170 can specify the feature region in which the same moving body is detected in different captured images included in the display moving image with higher accuracy.

  The image processing unit 360 acquires information on the feature region specified by the feature region specifying unit 354, the detection reliability calculated by the reliability calculation unit 356, and the relevance calculated by the relevance calculation unit 358. The image processing unit 360 may perform image processing for emphasizing the feature region specified by the feature region specifying unit 354 on the display moving image. For example, the image processing unit 360 may superimpose a marker such as a frame indicating the feature region specified by the feature region specifying unit 354 on the display moving image. Note that the image processing unit 360 may perform image processing for emphasizing the feature region identified by the feature region identifying unit 354 as the detection reliability calculated by the reliability calculation unit 356 is higher on the display moving image.

  In addition, the image processing unit 360 can visually distinguish a set of feature areas whose relevance calculated by the relevance calculation unit 358 is higher than a predetermined value from a set of other feature areas. May be superimposed on the display video. For example, the image processing unit 360 may superimpose a marker of the same color as a marker indicating a set of feature areas whose relevance is higher than a predetermined value on the display moving image. In addition, the image processing unit 360 may superimpose different color markers as markers indicating different sets of feature regions on the display moving image. The image processing unit 360 supplies the display moving image subjected to the image processing to the output unit 340.

  FIG. 6 shows an example of another block configuration of the compression unit 230. The compression unit 230 in this configuration compresses a plurality of captured images by a spatial scalable encoding process corresponding to the type of feature.

  The compression unit 230 in this configuration includes an image quality conversion unit 510, a difference processing unit 520, and an encoding unit 530. The difference processing unit 520 includes a plurality of inter-layer difference processing units 522a-d (hereinafter collectively referred to as inter-layer difference processing units 522). Encoding section 530 includes a plurality of encoders 532a-d (hereinafter collectively referred to as encoders 532).

  The image quality conversion unit 510 acquires a plurality of captured images from the image selection unit 220. In addition, the image quality conversion unit 510 acquires information specifying the feature region detected by the feature region detection unit 203 and information specifying the type of feature in the feature region. Then, the image quality conversion unit 510 duplicates the captured image, and generates captured images of the number of types of features in the feature area. Then, the image quality conversion unit 510 converts the generated captured image into an image having a resolution corresponding to the type of feature.

  For example, the image quality conversion unit 510 has a captured image converted to a resolution corresponding to the background area (hereinafter referred to as a low resolution image), and a captured image converted to the first resolution corresponding to the type of the first feature ( Called a first resolution image), a captured image converted to a second resolution corresponding to the second feature type (referred to as a second resolution image), and a third resolution corresponding to the third feature type. A captured image (referred to as a third resolution image) converted to is generated. Here, it is assumed that the first resolution image has a higher resolution than the low resolution image, the second resolution image has a higher resolution than the first resolution image, and the third resolution image has a higher resolution than the second resolution image.

  Then, the image quality conversion unit 510 converts the low resolution image, the first resolution image, the second resolution image, and the third resolution image into the inter-layer difference processing unit 522d, the inter-layer difference processing unit 522a, and the inter-layer difference processing unit 522b, respectively. And to the inter-layer difference processing unit 522c. The image quality conversion unit 510 supplies a moving image to each of the inter-layer difference processing units 522 by performing the above-described image quality conversion processing on each of the plurality of captured images.

  Note that the image quality conversion unit 510 may convert the frame rate of the moving image supplied to each of the inter-layer difference processing unit 522 in accordance with the feature type of the feature region. For example, the image quality conversion unit 510 may supply, to the inter-layer difference processing unit 522d, a moving image having a lower frame rate than the moving image supplied to the inter-layer difference processing unit 522a. In addition, the image quality conversion unit 510 may supply a moving image having a lower frame rate than the moving image supplied to the inter-layer difference processing unit 522b to the inter-layer difference processing unit 522a, and a frame lower than the moving image supplied to the inter-layer difference processing unit 522c. The rate movie may be supplied to the inter-tier difference processing unit 522b. Note that the image quality conversion unit 510 may convert the frame rate of the moving image supplied to the inter-layer difference processing unit 522 by thinning out the captured image according to the feature type of the feature region.

  The inter-layer difference processing unit 522d and the encoder 532d predictively encode a background area moving image including a plurality of low-resolution images. Specifically, the inter-layer difference processing unit 522 generates a difference image from a predicted image generated from another low-resolution image. Then, the encoder 532d quantizes the transform coefficient obtained by converting the difference image into a spatial frequency component, and encodes the quantized transform coefficient by entropy coding or the like. Note that such predictive encoding processing may be performed for each partial region of the low-resolution image.

  Further, the inter-layer difference processing unit 522a predictively encodes the first feature region moving image including the plurality of first resolution images supplied from the image quality conversion unit 510. Similarly, the inter-layer difference processing unit 522b and the inter-layer difference processing unit 522c each predictively encode a second feature area moving image including a plurality of second resolution images and a third feature area moving image including a plurality of third resolution images. To do. Hereinafter, specific operations of the inter-layer difference processing unit 522a and the encoder 532a will be described.

  The inter-layer difference processing unit 522a decodes the first resolution image encoded by the encoder 532d, and expands the decoded image to an image having the same resolution as the first resolution. Then, the inter-layer difference processing unit 522a generates a difference image between the enlarged image and the low resolution image. At this time, the inter-layer difference processing unit 522a sets the difference value in the background area to zero. Then, the encoder 532a encodes the difference image in the same manner as the encoder 532d. Note that the encoding processing by the inter-layer difference processing unit 522a and the encoder 532a may be performed for each partial region of the first resolution image.

  Note that, when the first resolution image is encoded, the inter-layer difference processing unit 522a calculates the code amount predicted when the difference image with the low resolution image is encoded, and the other first resolution image. The amount of code predicted when the difference image between the generated prediction image and the prediction image is encoded is compared. In the case where the latter code amount is smaller, the inter-layer difference processing unit 522a generates a difference image from the predicted image generated from the other first resolution image. In addition, the inter-layer difference processing unit 522a, when it is predicted that the code amount is smaller when encoded without taking the difference from the low-resolution image or the predicted image, It is not necessary to take a difference between them.

  Note that the inter-layer difference processing unit 522a does not have to set the difference value in the background area to zero. In this case, the encoder 532a may set the encoded data for difference information in an area other than the feature area to zero. For example, the encoder 532a may set the conversion coefficient after conversion to a frequency component to zero. Note that the motion vector information when the inter-layer difference processing unit 522d performs predictive encoding is supplied to the inter-layer difference processing unit 522a. The inter-layer difference processing unit 522a may calculate a motion vector for a predicted image using the motion vector information supplied from the inter-layer difference processing unit 522d.

  Note that the operations of the inter-layer difference processing unit 522b and the encoder 532b are that the second resolution image is encoded, and the first resolution after encoding by the encoder 532a when the second resolution image is encoded. The operations of the inter-layer difference processing unit 522b and the encoder 532b are substantially the same as the operations of the inter-layer difference processing unit 522a and the encoder 532a, except that the difference from the image may be taken. To do. Similarly, the operations of the inter-layer difference processing unit 522c and the encoder 532c are that the third resolution image is encoded, and when the third resolution image is encoded, after the encoding by the encoder 532b. Except for the fact that a difference from the second resolution image may be obtained, the operation is substantially the same as the operation of the inter-layer difference processing unit 522a and the encoder 532a, and thus the description thereof is omitted.

  As described above, the image quality conversion unit 510 generates, from each of the plurality of captured images, a low image quality image with a low image quality and a feature region image with higher image quality than the low image quality at least in the feature region. Then, the difference processing unit 520 generates a feature region difference image indicating a difference image between the feature region image in the feature region image and the feature region image in the low-quality image. Then, the encoding unit 530 encodes the feature region difference image and the low quality image.

  In addition, the image quality conversion unit 510 generates a low-quality image with reduced resolution from a plurality of captured images, and the difference processing unit 520 includes a feature region image in the feature region image and a feature region image in the low-quality image. A feature region difference image between the image and the image enlarged is generated. Further, the difference processing unit 520 has a spatial frequency component in which the difference between the feature region image and the enlarged image in the feature region is converted into the spatial frequency region, and the data amount of the spatial frequency component is in the region other than the feature region. A reduced feature area difference image is generated.

  As described above, the compression unit 230 performs hierarchical encoding by encoding image differences between a plurality of layers having different resolutions. As is clear from this, a part of the compression method by the compression unit 230 of this configuration is H.264. It is clear that a compression scheme according to H.264 / SVC is included.

  FIG. 7 shows an example of a captured image 700 captured by the imaging apparatus 100. A plurality of captured images 700-1 to 20-20 (hereinafter collectively referred to as captured images 700) indicate captured images captured by the image capturing apparatus 100. The imaging apparatus 100 may continuously capture the subject at an imaging rate higher than a predetermined reference imaging rate. For example, the imaging unit 102 may capture the subject at an imaging rate that is higher than the display rate that the display device 180 can display. In addition, the imaging unit 102 may continuously capture the subject at an imaging rate higher than a reference imaging rate that is determined in advance according to the movement speed of the monitoring target that is the monitoring target.

  It should be noted that the feature area detection unit 203 includes a feature area 710-1 to 20 (hereinafter collectively referred to as a feature area 710) and a feature area 720-1 to 20 (hereinafter collectively referred to as a feature area 720) from each of the captured images 700. ) Is detected. Then, the image selection unit 220 selects the captured images 700 to be output to the image processing apparatus 170 one by one from among a predetermined number of captured images 700 that are continuously captured from the captured images 700. .

  In this figure, the image selection unit 220 selects one captured image 700 to be output from each of a set including ten consecutive captured images 700 as an example. Specifically, the image selection unit 220 selects the captured image 700-5 from the captured images 700-1 to 700-10, and selects the captured image 700-15 from the captured images 700-11 to 20. Selected.

  The association processing unit 206 includes information indicating the positions of the feature regions 710-1 to 710-1 and the types of the feature regions, information indicating the positions of the feature regions 720-1 to 720-10 and the types of the feature regions, and the feature regions 710- Information indicating the imaging timing at which the captured images 700-1 to 10 in which 1 to 10 and the feature areas 720-1 to 10 are detected is captured is appended to the information for identifying the captured image 700-5. Note that the information for identifying the captured image 700-5 may be a frame ID for identifying the captured image 700-5 in the captured moving image including the captured image 700, and is included in the captured image 700 output to the image processing apparatus 170. It may be a frame ID for identifying the captured image 700-5. Similarly, the association processing unit 206 includes information indicating the position of the feature region 710-11 to 20 and the type of the feature region, information indicating the position of the feature region 720-11 to 20 and the type of the feature region, and their features. Information indicating the imaging timing at which the captured images 700-11 to 20 where the areas 710-11 to 20 and the characteristic areas 720-11 to 20 are detected is appended to the information for identifying the captured image 700-15.

  FIG. 8 shows an example of information attached to the captured image 700-5. The association processing unit 206 attaches coordinates indicating the positions of the feature region 710-5 and the feature region 720-5 as an example of information indicating the positions of the feature region 710-5 and the feature region 720-5. Specifically, when the feature region 710-5 and the feature region 720-5 are rectangular, the association processing unit 206 attaches the coordinates of the vertices P1, P2, P3, and P4 on the diagonal of the rectangle. It should be noted that the shape and size of the feature region of the rectangle are simultaneously defined by the information indicating the diagonal of the rectangle.

  Further, the association processing unit 206 may attach an identification number for identifying the type of feature. For example, the association processing unit 206 attaches information indicating type = 2 as the identification number of the feature region 710-5 and information indicating type = 1 as the identification number of the feature region 720-5. In addition, the association processing unit 206 accompanies time t = xxx when the captured image 700-5 is captured. In the above description, the operation of the association processing unit 206 has been described by taking as an example the case where the association processing unit 206 appends information on the feature area detected from the captured image 700-5. The processing unit 206 also attaches the same information to the feature region information detected from the other captured image 700 associated with the captured image 700-5.

  FIG. 9 shows an image region in which the feature region specifying unit 354 specifies the feature region. The feature region specifying unit 354 acquires the position information of the feature region attached to the captured image 900 selected by the image selection unit 220 from the association analysis unit 302. In the following description, the centroid position 901, the centroid position 902, the centroid position 904, and the centroid position 905 of the feature area calculated from the coordinates of the diagonal vertices of the rectangle of the feature area attached to the captured image 900 are used. The operation of the feature region specifying unit 354 will be described.

  In the example of this figure, it is assumed that the feature region detection unit 203 cannot detect the feature region from the captured image 900. That is, the barycentric position 901, the barycentric position 902, the barycentric position 904, and the barycentric position 905 indicate the barycentric positions of the feature areas detected from the captured images that are not selected by the image selection unit 220. The feature area specifying unit 354 detects from the above information indicating the imaging timing that the centroid of the feature area has moved in time in the order of the centroid position 901, the centroid position 902, the centroid position 904, and the centroid position 905. To do. Therefore, the feature region specifying unit 354 specifies the feature region from the image region 910 including the center of gravity position 902 and the point existing between the center of gravity position 904.

  Note that the feature region specifying unit 354 may specify the coordinates of the centroid position of the feature region at the timing when the captured image 900 is captured from the coordinates of the centroid position 901, the centroid position 902, the centroid position 904, and the centroid position 905. . Then, the feature region specifying unit 354 may specify the feature region from the image region 910 including the point indicated by the specified coordinates.

  The feature region specifying unit 354 can estimate the size and shape of the feature region from the coordinates of the diagonal corner vertices of the feature region attached to the captured image 900. Then, the feature region specifying unit 354 may specify the feature region from the larger image region 910 when the size of the specified feature region is larger. For example, the feature region specifying unit 354 may specify the feature region from the larger image region 910 when the maximum value or the average value of the sizes of the plurality of feature regions is larger. The feature region specifying unit 354 may specify the feature region from the image region 910 having a shape similar to the shape of the specified feature region.

  As described above, the feature region specifying unit 354 can limit the region for specifying the feature region. For this reason, the feature region specifying unit 354 can specify the feature region at high speed.

  FIG. 10 shows another example of the position of the center of gravity calculated from the position of the feature region attached to the captured image 1000. The barycentric position 1001, the barycentric position 1002, the barycentric position 1004, and the barycentric position 1005 indicate the barycentric position of the feature area detected from the captured image that has not been selected by the image selecting unit 220. A centroid position 1003 indicates the centroid position of the feature region detected in the captured image 1000.

  In this case, the reliability calculation unit 356 calculates the detection reliability of the feature region detected from the captured image 1000 based on the difference in the centroid position of the feature region attached to the captured image 1000. The reliability calculation unit 356 estimates the position of the feature region to be detected from the captured image 1000 from the movement direction of the feature region estimated from the centroid position 1001, the centroid position 1002, the centroid position 1004, and the centroid position 1005. Then, the difference between the estimated gravity center position of the feature region and the gravity center position 1003 is calculated. The reliability calculation unit 356 calculates the detection reliability of the feature region detected from the captured image 1000 higher when the magnitude of the position difference is larger.

  In this way, the reliability calculation unit 356 can easily calculate the detection reliability. Thus, the image processing unit 360 can perform appropriate image processing according to the detection reliability. Note that the feature region specifying unit 354 may specify the feature region from a wider image region in the captured image 1000 when the detection reliability detected by the reliability calculation unit 356 is lower than a predetermined value. Thereby, the probability that the feature region specifying unit 354 fails to specify the feature region can be reduced.

  FIG. 11 shows an example of the barycentric position calculated from the respective positions of a plurality of types of feature regions attached to the captured image. The captured image 1101, the captured image 1102, and the captured image 1103 are captured images selected by the image selection unit 220. It is assumed that the centroid position of the first type feature area detected from the captured image 1101 is the centroid position 1111, and the centroid position of the second type feature area detected from the captured image 1101 is the centroid position 1121.

  In addition, it is assumed that the barycentric position of the first type feature area detected from the captured image 1102 is the barycentric position 1112, and the barycentric position of the second type of characteristic area detected from the captured image 1102 is the barycentric position 1122. Further, it is assumed that the centroid position of the first type feature area detected from the captured image 1103 is the centroid position 1113, and the centroid position of the second type feature area detected from the captured image 1103 is the centroid position 1123.

  It should be noted that the relevance calculation unit 358 calculates from the positions of the feature regions detected from the captured images that are associated with the captured image 1101, the captured image 1102, and the captured image 1103 and that are not selected by the image selection unit 220. The center of gravity position can be calculated. In the figure, the positions of the centers of gravity are indicated by crosses without symbols.

  The relevance calculation unit 358 calculates the relevance of the feature areas detected from the captured image 1101, the captured image 1102, and the captured image 1103 from the continuity of the calculated center of gravity position. In the example of this figure, more centroid positions are arranged near the line connecting the centroid position 1111, the centroid position 1112, and the centroid position 1113. In such a case, the relevance calculation unit 358 calculates the relevance among the plurality of feature regions having the centroid position at the centroid position 1111, the centroid position 1112, and the centroid position 1113. Similarly, the relevancy calculation unit 358 calculates the relevance between the centroid position 1121, the centroid position 1122, and a plurality of feature regions having the centroid position at the centroid position 1123 higher.

  As described above, the relevance calculation unit 358 can appropriately calculate the relevance of the feature regions in the plurality of captured images. For this reason, the image processing unit 360 can quickly distinguish between, for example, even when a moving image in which a person and a vehicle cross each other is sent. In some cases, the image processing unit 360 can distinguish between the two without performing image recognition. Further, the image processing unit 360 can perform a marking process on the captured image so that a feature region having low relevance can be distinguished at a glance.

  FIG. 12 shows an example of the collection area in the captured image 1200. In this figure, the feature region 1210, the feature region 1220, and the feature region 1230 are selected from any one of the captured image that is not selected by the image selection unit 220 and the captured image that is selected by the image selection unit 220. The characteristic area detected by is shown. Note that the detection reliability of the feature region 1210 is p1, the detection reliability of the feature region 1220 is p2, and the detection reliability of the feature region 1230 is p3. It is assumed that the detection reliability is higher as the size is larger.

  The importance calculation unit 240 calculates the importance for each of the regions included in the feature region 1210, the feature region 1220, and the feature region 1230 that are included in different ways. For example, the importance degree calculation unit 240 includes a feature region 1210, a feature region 1220, and a set region of the feature region 1230 in the first region in the feature region 1210 that is not included in the other feature regions and in the other feature regions. A second region in the feature region 1220 not included, a third region in the feature region 1230 not included in the other feature region, a fourth region included only in the feature region 1210 and the feature region 1220, a feature region 1210 and It is divided into a fifth area included only in the feature area 1230, a sixth area included only in the feature area 1220 and the feature area 1230, and a seventh area included in the feature area 1210, the feature area 1220, and the feature area 1230.

  Then, the importance calculation unit 240 calculates the importance for each of the seven areas. Specifically, the importance level calculation unit 240 calculates a total value obtained by summing the detection reliability levels of the feature areas including the respective areas for each of the seven areas. The importance calculation unit 240 calculates a greater importance as the total value is larger.

  Then, the association processing unit 206 may associate information indicating an area in which importance greater than a predetermined value is calculated with the captured image 1200 selected by the image selection unit 220. For example, the association processing unit 206 may associate information indicating the fourth region, the sixth region, and the seventh region indicated by hatching in the drawing with respect to the selected captured image 1200. . In addition, the compression unit 230 treats an area whose importance is greater than a predetermined value as the feature area described with reference to FIG. 3 or FIG. 6, so that the compressed moving image has higher image quality than the other areas. May be generated.

  As described above, since the region in which the importance calculated by the importance calculation unit 240 is greater than a predetermined value is output in association with the captured image, the image processing apparatus 170 performs image recognition to identify the feature region. The area can be further narrowed. For this reason, the feature region can be identified at a higher speed in the image processing apparatus 170.

  FIG. 13 shows an example of an image processing system 20 according to another embodiment. The configuration of the image processing system 20 in the present embodiment is the image described with reference to FIG. 1 except that the imaging devices 100a-d have image processing units 804a-d (hereinafter collectively referred to as image processing units 804). The configuration of the processing system 10 is the same.

  Each of the image processing units 804 has the same configuration as that of the image processing unit 200. The functions and operations of the constituent elements included in the image processing unit 804 are changed to that the constituent elements included in the image processing unit 200 process the captured moving image obtained by the decompression processing by the compressed moving image decompression unit 202. The functions and operations of each component included in the image processing unit 200 may be substantially the same except that the captured moving image captured by the imaging unit 102 is processed. Also in the image processing system 20 having such a configuration, substantially the same effect as that described in relation to the image processing system 10 from FIGS. 1 to 13 can be obtained.

  FIG. 14 shows an example of the hardware configuration of the image processing device 120 and the image processing device 170. The image processing device 120 and the image processing device 170 include a CPU peripheral unit, an input / output unit, and a legacy input / output unit. The CPU peripheral section includes a CPU 1505, a RAM 1520, a graphic controller 1575, and a display device 1580 that are connected to each other by a host controller 1582. The input / output unit includes a communication interface 1530, a hard disk drive 1540, and a CD-ROM drive 1560 that are connected to the host controller 1582 by the input / output controller 1584. The legacy input / output unit includes a ROM 1510, a flexible disk drive 1550, and an input / output chip 1570 connected to the input / output controller 1584.

  The host controller 1582 connects the RAM 1520, the CPU 1505 that accesses the RAM 1520 at a higher transfer rate, and the graphic controller 1575. The CPU 1505 operates according to the contents of the programs stored in the ROM 1510 and the RAM 1520 and controls each unit. The graphic controller 1575 acquires image data generated by the CPU 1505 or the like on a frame buffer provided in the RAM 1520 and displays the image data on the display device 1580. Alternatively, the graphic controller 1575 may include a frame buffer that stores image data generated by the CPU 1505 or the like.

  The input / output controller 1584 connects the host controller 1582 to the hard disk drive 1540, the communication interface 1530, and the CD-ROM drive 1560, which are relatively high-speed input / output devices. The hard disk drive 1540 stores programs and data used by the CPU 1505. The communication interface 1530 is connected to the network communication device 1598 to transmit / receive programs or data. The CD-ROM drive 1560 reads a program or data from the CD-ROM 1595 and provides it to the hard disk drive 1540 and the communication interface 1530 via the RAM 1520.

  The input / output controller 1584 is connected to the ROM 1510, the flexible disk drive 1550, and the relatively low-speed input / output device of the input / output chip 1570. The ROM 1510 stores a boot program that is executed when the image processing device 120 and the image processing device 170 are started, or a program that depends on the hardware of the image processing device 120 and the image processing device 170. The flexible disk drive 1550 reads a program or data from the flexible disk 1590 and provides it to the hard disk drive 1540 and the communication interface 1530 via the RAM 1520. The input / output chip 1570 connects various input / output devices via the flexible disk drive 1550 or a parallel port, serial port, keyboard port, mouse port, and the like.

  A program executed by the CPU 1505 is stored in a recording medium such as the flexible disk 1590, the CD-ROM 1595, or an IC card and provided by the user. The program stored in the recording medium may be compressed or uncompressed. The program is installed in the hard disk drive 1540 from the recording medium, read into the RAM 1520, and executed by the CPU 1505. The program executed by the CPU 1505 causes the image processing apparatus 120 to function as each component included in the image processing apparatus 120 described with reference to FIGS. 1 to 13, and causes the image processing apparatus 170 to function as illustrated in FIGS. 1 to 14. It is made to function as each component which image processing apparatus 170 which was explained related has.

  The program shown above may be stored in an external storage medium. As the storage medium, in addition to the flexible disk 1590 and the CD-ROM 1595, an optical recording medium such as a DVD or PD, a magneto-optical recording medium such as an MD, a tape medium, a semiconductor memory such as an IC card, or the like can be used. Further, a storage device such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet is used as a recording medium, and is provided to the image processing device 120 and the image processing device 170 as a program via the network. Also good. As described above, the computer controlled by the program functions as the image processing device 120 and the image processing device 170.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

1 is a diagram illustrating an example of an image processing system 10 according to an embodiment. 2 is a diagram illustrating an example of a block configuration of an image processing apparatus 120. FIG. 3 is a diagram illustrating an example of a block configuration of a compression unit 230. FIG. 2 is a diagram illustrating an example of a block configuration of an image processing apparatus 170. FIG. 5 is a diagram illustrating an example of a block configuration of a feature area processing unit 350. FIG. It is a figure which shows an example of the other block structure of the compression part 230. FIG. 6 is a diagram illustrating an example of a captured image 700 captured by the imaging apparatus 100. FIG. It is a figure which shows an example of the information incidental to the captured image 700-5. It is a figure which shows the image area which the characteristic area specific | specification part 354 specifies a characteristic area. It is a figure which shows another example of the gravity center position calculated from the position of the characteristic area. It is a figure which shows an example of the gravity center position calculated from each position of multiple types of feature area. It is a figure which shows an example of the collection area | region in a captured image. It is a figure which shows an example of the image processing system 20 which concerns on other embodiment. 2 is a diagram illustrating an example of a hardware configuration of an image processing device 120 and an image processing device 170. FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Image processing system 20 Image processing system 100 Imaging device 102 Imaging part 104 Imaging moving image compression part 110 Communication network 120 Image processing apparatus 130 Person 140 Mobile body 150 Monitoring object space 160 Space 170 Image processing apparatus 175 Image DB
180 Display Device 200 Image Processing Unit 201 Compressed Movie Acquisition Unit 202 Compressed Movie Expansion Unit 203 Feature Area Detection Unit 206 Association Processing Unit 207 Output Unit 210 Compression Control Unit 220 Image Selection Unit 230 Compression Unit 232 Image Dividing Unit 234 Fixed Value Unit 236 Compression processing unit 240 Importance calculation unit 250 Image acquisition unit 301 Compressed image acquisition unit 302 Association analysis unit 310 Decompression control unit 320 Decompression unit 322 Decoder 330 Synthesis unit 340 Output unit 350 Feature region processing unit 352 Feature region image selection unit 354 Feature region identification unit 356 Reliability calculation unit 358 Relevance calculation unit 360 Image processing unit 510 Image quality conversion unit 520 Difference processing unit 522 Inter-layer difference processing unit 530 Encoding unit 532 Encoder 700 Captured image 710 Feature region 720 Feature region 804 Image processing unit 900 captured image 901 centroid position 902 Centroid position 904 centroid position 905 centroid position 910 image area 1000 captured image 1000 captured image 1001 centroid position 1002 centroid position 1004 centroid position 1004 centroid position 1005 centroid position 1101 captured image 1102 captured image 1103 captured image 1111 centroid position 1112 centroid position 1121 centroid position 1122 Center of gravity position 1113 Center of gravity position 1123 Center of gravity position 1200 Captured image 1210 Feature region 1220 Feature region 1230 Feature region 1505 CPU
1510 ROM
1520 RAM
1530 Communication interface 1540 Hard disk drive 1550 Flexible disk drive 1560 CD-ROM drive 1570 Input / output chip 1575 Graphic controller 1580 Display device 1582 Host controller 1584 Input / output controller 1590 Flexible disk 1595 CD-ROM
1598 Network communication device

Claims (19)

An image acquisition unit that acquires a plurality of captured images captured at different timings;
A feature region detector that detects a feature region from each of the plurality of captured images;
An image selection unit for selecting a part of the captured images from the plurality of captured images;
An image processing system comprising: an output unit that outputs information indicating a region including a plurality of the feature regions detected from the plurality of captured images in association with the part of the captured images . An image acquisition unit that acquires a plurality of captured images captured at different timings;
A feature region detector that detects a feature region from each of the plurality of captured images;
An image selection unit for selecting a part of the captured images from the plurality of captured images;
An output unit that outputs information specifying a feature region detected from a captured image other than the partial captured image among the plurality of captured images in association with the partial captured image;
An image processing system comprising: The output unit, an image processing system according to claim 1 or 2 the information indicating the set area of the plurality of detected plurality of feature regions from the captured image, and outputs in association with the portion of the captured image . Based on the detection reliability of each of the plurality of feature regions detected by the feature region detection unit, the importance is determined for each of the plurality of image regions in the set region of the plurality of feature regions detected from the plurality of captured images. Further comprising an importance calculation unit for calculating,
The image processing according to claim 3, wherein the output unit outputs information indicating an image region whose importance calculated by the importance calculation unit is larger than a predetermined value in association with the part of the captured images. system. The importance calculation unit calculates an addition value obtained by adding the detection reliability of the feature area including the image area for each of a plurality of image areas in the collection area, and based on the addition value, a plurality of importance values in the collection area are calculated. The image processing system according to claim 4, wherein the importance is calculated for each image region. Based on the detection reliability of each of the plurality of feature regions detected by the feature region detection unit, the importance is determined for each of the plurality of image regions in the set region of the plurality of feature regions detected from the plurality of captured images. Further comprising an importance calculation unit for calculating,
The image processing system according to claim 3, wherein the output unit outputs the information indicating the collection area and the importance calculated by the importance calculation unit in association with the part of the captured images. The image processing system according to claim 5, wherein the feature region detection unit detects a region including an object indicating a moving object as the feature region. The image processing system according to claim 7, wherein the image selection unit selects the partial captured image that satisfies a predetermined condition from the plurality of captured images. The image processing system according to claim 8, wherein the image selection unit selects the part of the captured images in which the image content of the feature region detected by the feature region detection unit meets a predetermined condition. 4. The image processing apparatus according to claim 3, further comprising a feature region specifying unit that specifies a feature region from the partial captured image based on information indicating the collective region output in association with the partial captured image from the output unit. The image processing system described. The compression unit according to claim 3, further comprising: a compression unit that compresses the image of the collective region in the part of the captured images and the image of the background region other than the collective region in the captured image at different compression rates. Image processing system. The compression unit is
An image dividing unit that divides the part of the captured image into the collective region and a background region other than the collective region;
The image processing system according to claim 11, further comprising: a compression processing unit that compresses the feature region image that is the image of the collective region and the background region image that is the image of the background region with different intensities. The image dividing unit divides each of the plurality of captured images included in the moving image as moving image constituent images into the collective region and the background region,
The image processing system according to claim 12, wherein the compression processing unit compresses a feature area moving image including a plurality of the characteristic area images and a background area moving image including a plurality of the background area images with different strengths. The compression unit is
A low-quality image having a low image quality and a feature area image having a higher image quality than the low-quality image in at least the feature area are generated from each of the plurality of captured images included in the moving image as a moving image constituent image. An image generator;
A difference processing unit that generates a feature area difference image indicating a difference image between the image of the feature area in the feature area image and the image of the feature area in the low-quality image;
The image processing system according to claim 11, further comprising: an encoding unit that encodes the feature area difference image and the low-quality image. The image generation unit generates the low-quality image with reduced resolution from a plurality of the captured images included in the moving image as a moving image constituent image,
The difference processing unit generates the feature region difference image between an image of the feature region in the feature region image and an image obtained by enlarging the image of the feature region in the low-quality image. Image processing system. The difference processing unit has a spatial frequency component in which a difference between the feature region image and the enlarged image is converted into a spatial frequency region in the feature region, and the spatial frequency component of the region other than the feature region The image processing system according to claim 15, wherein the feature area difference image with a reduced data amount is generated. The program for functioning a computer as an image processing system as described in any one of Claims 1-16. An image acquisition stage for acquiring a plurality of captured images captured at different timings;
A feature region detection step of detecting a feature region from each of the plurality of captured images;
An image selection step of selecting a part of the captured images from the plurality of captured images;
An image processing method comprising: an output step of outputting information indicating a region including the plurality of feature regions detected from the plurality of captured images in association with the part of the captured images. An image acquisition stage for acquiring a plurality of captured images captured at different timings;
A feature region detection step of detecting a feature region from each of the plurality of captured images;
An image selection step of selecting a part of the captured images from the plurality of captured images;
An output stage for outputting information specifying a feature region detected from a captured image other than the partial captured image among the plurality of captured images in association with the partial captured image;
An image processing method comprising:

Images