CN104102068B - Autofocus method and autofocus device - Google Patents

Abstract

The invention provides an automatic focusing method and an automatic focusing device, which are suitable for the automatic focusing device, and the method comprises the following steps: selecting a target object, and shooting the target object by using a first image sensor and a second image sensor to generate a first image and a second image; performing three-dimensional depth estimation according to the first image and the second image to generate a three-dimensional depth map; optimizing the three-dimensional depth map to generate an optimized three-dimensional depth map; judging depth information corresponding to the target object according to the optimized three-dimensional depth map, and acquiring a focusing position of the target object according to the depth information; and driving the automatic focusing device to execute an automatic focusing program according to the focusing position.

Description

Autofocus method and autofocus device

technical field

The present invention relates to an automatic focusing technology, and in particular to an automatic focusing method and an automatic focusing device using a stereo vision image processing technology.

Background technique

Digital cameras have a fairly sophisticated and complex mechanical structure, which further enhances the functionality and handling of the camera. In addition to factors such as the shooting skills of the user and the influence of the surrounding environment, the built-in Auto Focus (AF) system of the digital camera also has a considerable impact on the quality of the captured image.

Generally speaking, auto-focus technology means that a digital camera will move the lens to change the distance between the lens and the subject, and corresponding to different lens positions to calculate the focus evaluation value of the subject picture (hereinafter referred to as the focus value) , until the maximum focus value is found. Specifically, the maximum focus value of the lens means that the subject image with the maximum definition can be obtained corresponding to the current position of the lens. However, in the hill-climbing or regression method (regression) used in the current autofocus technology, the continuous movement of the lens and the search time for the maximum focus value require several images to achieve one focus, so it is easy Takes a lot of time. In addition, in the process of moving the lens of the digital camera, the head may be moved too much, and the lens needs to be moved back and forth. In this way, the edge part of the picture may enter and exit the picture, which is the breathing phenomenon of the lens picture, and This phenomenon destabilizes the picture.

On the other hand, there is an existing stereo vision technology for image processing and automatic focus technology to establish three-dimensional depth information of the image, which can effectively reduce the time-consuming focus and the breathing phenomenon of the picture, and can improve the focus speed and picture stability. attention in related fields. However, generally speaking, the current stereo vision technology image processing often fails to accurately locate the position of each point in the image when obtaining the three-dimensional seat position information of each pixel point in the image. Since it is difficult to identify the relative depth in areas without texture and flat areas, it is difficult to accurately obtain the depth information of each point, which may cause holes in the 3D depth map. In addition, if the autofocus technology is applied to handheld electronic devices (such as smart phones), in order to reduce the size of the product, its stereo vision baseline (stereo baseline) usually needs to be reduced as much as possible, so that accurate positioning will be more difficult , and may lead to the increase of holes on the 3D depth map, thereby affecting the execution of subsequent image focusing procedures.

Contents of the invention

The invention provides an automatic focusing method and an automatic focusing device, which have fast focusing speed and good picture stability.

An autofocus method of the present invention is applicable to an autofocus device, wherein the autofocus device includes first and second image sensors. The autofocus method includes the following steps. A target is selected, and the first and second image sensors are used to photograph the target to generate a first image and a second image. And perform 3D depth estimation according to the first image and the second image to generate a 3D depth map. In addition, optimization processing is performed on the three-dimensional depth map to generate an optimized three-dimensional depth map. The depth information corresponding to the target object is judged based on the optimized three-dimensional depth map, and the focus position of the target object is obtained according to the depth information. Then, the auto-focus device is driven to execute an auto-focus program according to the focus position.

An automatic focusing device of the present invention includes first and second image sensors, a focusing module and a processing unit. The first and second image sensors photograph the target to generate a first image and a second image. The focus module controls focus positions of the first and second image sensors. The processing unit is coupled to the first and second image sensors and the focusing module. The processing unit performs 3D depth estimation on the first image and the second image to generate a 3D depth map, and optimizes the 3D depth map to generate an optimized 3D depth map. The processing unit judges the depth information corresponding to the target object according to the optimized three-dimensional depth map, and obtains the focus position of the target object according to the depth information, and the focus module executes the auto-focus procedure according to the focus position.

In an embodiment of the present invention, the step of obtaining the focus position of the target according to the depth information includes: querying a depth comparison table according to the depth information to obtain the focus position of the target.

In an embodiment of the present invention, the above-mentioned method for selecting a target includes: receiving a click signal from a user for selecting a target through an auto-focus device or performing a target detection procedure by the auto-focus device to automatically select a target, And obtain the coordinate position of the target object.

In an embodiment of the present invention, the above-mentioned step of judging the depth information corresponding to the target object based on the optimized 3D depth image and obtaining the focus position includes: selecting a block including the target object, and reading a plurality of neighbors in the block The depth information of the neighborhood pixels is statistically calculated on the depth information of these neighborhood pixels to obtain the optimized depth information of the target object. Moreover, the focus position of the target object is obtained according to the optimized depth information.

In an embodiment of the present invention, the above-mentioned autofocus method further includes: executing an object tracking program on the target to obtain at least one feature information and motion trajectory of the target, wherein the feature information includes center of gravity, color, area, and contour or shape information.

In an embodiment of the present invention, the above-mentioned auto-focusing method further includes: storing depth information of the target object at different time points in a depth information database. And, the movement amount estimation is performed according to the depth information in the depth information database, so as to obtain the depth variation trend of the target object.

In an embodiment of the present invention, the above optimization process is Gaussian smoothing process.

In an embodiment of the present invention, the above-mentioned auto-focus device further includes a storage unit. The storage unit is coupled to the processing unit for storing the first and second images and the depth comparison table. The processing unit queries the depth comparison table according to the depth information to obtain the focus position of the target object.

In an embodiment of the present invention, the above processing unit further includes a block depth estimator. The block depth estimator selects the block containing the target object, reads the depth information of multiple neighboring pixels in the block, and performs statistical calculations on the depth information of these neighboring pixels to obtain the optimized depth information of the target object , and obtain the focus position of the target object according to the optimized depth information.

In an embodiment of the present invention, the above processing unit further includes an object tracking module. The object tracking module is coupled to the block depth estimator, and tracks the object to obtain at least one feature information and motion trajectory, wherein the feature information includes center of gravity, color, area, outline or shape information, so that the block depth estimator can use at least one A statistical operation is performed on the feature information and the depth information of these neighboring pixels.

In an embodiment of the present invention, the above-mentioned storage unit further includes a depth information database, and the processing unit further includes a movement amount prediction module. The depth information database is used to store the depth information of the target object at different time points. The movement amount prediction module is coupled to the storage unit and the focus module, and performs movement amount prediction according to the depth information in the depth information database, so as to obtain the depth change trend of the target object, so that the focus module controls the first and second image sensors according to Smooth movement of the depth change trend.

Based on the above, the autofocus method and autofocus device provided by the present invention apply the image processing technology of stereo vision and optimize the three-dimensional depth map generated by it to obtain the focus position, which only takes one image. Complete the execution of the relevant autofocus steps. Therefore, the auto-focus device and the auto-focus method of the present invention have a fast focusing speed. In addition, since there is no need to search, it will not cause the picture to breathe, and has good stability.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail with reference to the accompanying drawings.

Description of drawings

Fig. 1 is a block diagram of an automatic focusing device shown in an embodiment of the present invention;

Fig. 2 is a flow chart of an autofocus method shown in an embodiment of the present invention;

Fig. 3 is a block diagram of a storage unit and a processing unit in the embodiment of Fig. 1;

FIG. 4 is a flow chart of an autofocus method shown in another embodiment of the present invention;

Fig. 5 is a flow chart of steps for judging the optimal depth information of an object in the embodiment of Fig. 4;

Fig. 6 is a flow chart of an autofocus method shown in yet another embodiment of the present invention.

Explanation of reference signs:

100: automatic focus device;

110: a first image sensor;

120: the second image sensor;

130: focusing module;

140: storage unit;

141: depth information database;

150: processing unit;

151: block depth estimator;

153: object tracking module;

155: movement amount prediction module;

S110, S120, S130, S140, S150, S151, S152, S160, S410, S610, S620: steps.

detailed description

FIG. 1 is a block diagram of an automatic focusing device according to an embodiment of the present invention. Referring to FIG. 1 , the autofocus device 100 of this embodiment includes a first image sensor 110 and a second image sensor 120 , a focusing module 130 , a storage unit 140 and a processing unit 150 . In this embodiment, the auto-focus device 100 is, for example, a digital camera, a digital video camera (Digital Video Camcorder, DVC) or other handheld electronic devices that can be used for video or photography functions, but the scope of the invention is not limited thereto.

Referring to FIG. 1 , in this embodiment, the first and second image sensors 110 and 120 may include components such as lenses, photosensitive elements, or apertures to acquire images. In addition, the focusing module 130, the storage unit 140, and the processing unit 150 may be functional modules implemented by hardware and/or software, wherein the hardware may include a central processing unit, a chipset, a microprocessor, and other hardware devices with image processing functions or The combination of the above-mentioned hardware devices, and the software can be the operating system, drivers, etc. In this embodiment, the processing unit 150 is coupled to the first and second image sensors 110, 120, the focusing module 130 and the storage unit 140, and can be used to control the first and second image sensors 110, 120 and the focusing module 130, and The relevant information is stored in the storage unit 140 . The functions of each module of the autofocus device 100 of this embodiment will be described in detail below with reference to FIG. 2 .

FIG. 2 is a flow chart of an autofocus method shown in an embodiment of the present invention. Referring to FIG. 2 , in this embodiment, the auto-focus method can be implemented by using the auto-focus device 100 in FIG. 1 , for example. The detailed steps of the auto-focus method of this embodiment will be further described below together with each module in the auto-focus device 100 .

Firstly, step S110 is executed to select a target object. Specifically, in the present embodiment, the method for selecting the target may, for example, receive a click signal from the user for selecting the target through the auto-focus device 100 , so as to select the target. For example, the user can touch or move the imaging device to a specific area to select the target, but the invention is not limited thereto. In other feasible embodiments, the method of selecting the target can also be performed by the auto-focus device 100 to perform a target detection process to automatically select the target and obtain the coordinate position of the target. For example, the auto-focus device 100 can automatically select the target by using face detection, smile detection, or subject detection technology, and obtain its coordinate position. However, the present invention is not limited thereto, and those skilled in the art can design the modes for selecting objects in the auto-focus device 100 according to actual needs, and details will not be repeated here.

Next, step S120 is executed, using the first image sensor 110 and the second image sensor 120 to photograph the target object, so as to generate a first image and a second image respectively. For example, the first image is, for example, the image for the left eye, and the second image is, for example, the image for the right eye. In this embodiment, the first image and the second image can be stored in the storage unit 140 for use in subsequent steps.

Next, step S130 is executed, the processing unit 150 performs 3D depth estimation according to the first image and the second image to generate a 3D depth map. Specifically, the processing unit 150 may perform image processing through stereo vision technology to obtain the three-dimensional coordinate position of the object in space and the depth information of each point in the image. And after obtaining the preliminary depth information of each point, all the depth information is integrated into a three-dimensional depth map.

Next, step S140 is executed, and the processing unit 150 optimizes the 3D depth map to generate an optimized 3D depth map. Specifically, in this embodiment, the method for performing optimization processing is, for example, to use image processing technology to perform weighting processing on the depth information of each point and its adjacent depth information. For example, in this embodiment, the optimization process may be Gaussian smoothing process. In short, in Gaussian smoothing, the pixel value of each point is the weighted average of the surrounding adjacent pixel values, and the value of the original pixel has the largest Gaussian distribution value, so it has the largest weight, and the adjacent pixels increase with the distance from the original As pixels get farther away, their weights get smaller and smaller. Therefore, after the processing unit 150 performs Gaussian smoothing on the three-dimensional depth map, the depth information of each point of the image will be relatively continuous, and at the same time, the depth information of the edge can be preserved. In this way, in addition to avoiding the problem that the depth information of each point recorded in the original 3D depth map may be inaccurate or discontinuous, the depth of the adjacent area can also be used for the holes that originally existed on the 3D depth map. Information is patched to achieve a complete condition. However, it should be noted that although the above-mentioned optimization processing method is described using Gaussian smoothing processing as an example, the present invention is not limited thereto. In other feasible embodiments, those with ordinary knowledge in this technical field may select other appropriate statistical calculation methods to perform the optimization process according to actual needs, so details will not be repeated here.

Next, step S150 is executed again, the processing unit 150 judges the depth information corresponding to the target object according to the optimized 3D depth map, and obtains the focus position of the target object according to the depth information. Specifically, the step of obtaining the focus position of the target according to the depth information is, for example, querying a depth comparison table according to the depth information to obtain the focus position of the target. For example, the process of executing the auto-focus program may be to control the stepping motor steps (step) or the current value of the voice coil motor in the auto-focus device 100 through the focus module 130 to adjust the first and second image sensors 110, 120 respectively. zoom lens to the desired focus position to focus. Therefore, through the calibration process of the stepping motor or voice coil motor in advance, the corresponding relationship between the number of steps of the stepping motor or the current value of the voice coil motor and the clear depth of the target can be obtained in advance, and the results can be compiled into a depth comparison Table, and stored in the storage unit 140. In this way, the number of steps of the stepping motor or the current value of the voice coil motor corresponding to the depth information can be queried based on the currently obtained depth information of the target, and the focus position information of the target can be obtained accordingly.

Next, step S160 is executed, and the processing unit 150 drives the auto-focus device 100 to execute an auto-focus procedure according to the focus position. Specifically, since the focus module 130 controls the focus positions of the first and second image sensors 110, 120, the processing unit 150 can drive the focus module 130 of the auto-focus device 100 after obtaining the focus position information about the target object, And thereby adjust the zoom lenses of the first and second image sensors 110 and 120 to focus positions to complete autofocus.

In this way, the method of generating a three-dimensional depth map by applying the above-mentioned image processing technology of stereo vision, and then optimizing the three-dimensional depth map to obtain the focus position will make the auto-focus device 100 and the auto-focus method of this embodiment Only one image time is required to complete the execution of the relevant autofocus steps. Therefore, the auto-focus device 100 and the auto-focus method of this embodiment have a fast focusing speed. In addition, since there is no need to search, it will not cause the picture to breathe, and has good stability.

FIG. 3 is a block diagram of a processing unit and a storage unit in the embodiment of FIG. 1 . Please refer to FIG. 3 , in more detail, the storage unit 140 of the autofocus device 100 of this embodiment also includes a depth information database 141, and the processing unit 150 also includes a block depth estimator 151, an object tracking module 153 and a moving amount prediction module 155 . In this embodiment, the block depth estimator 151, the object tracking module 153 and the movement amount prediction module 155 may be functional modules implemented by hardware and/or software, wherein the hardware may include a central processing unit, a chipset, a microprocessor The hardware device with image computing and processing functions such as a device or a combination of the above hardware devices, and the software can be an operating system, a driver, and the like. The functions of the block depth estimator 151 , the object tracking module 153 , the movement amount prediction module 155 and the depth information database 141 of this embodiment will be described in detail below with reference to FIGS. 4 to 6 .

Fig. 4 is a flow chart of an auto-focus method according to another embodiment of the present invention. Please refer to FIG. 4 , in this embodiment, the auto-focus method can be implemented by using the auto-focus device 100 in FIG. 1 and the processing unit 150 in FIG. 3 , for example. The autofocus method in this embodiment is similar to the autofocus method in the embodiment in FIG. 2 , and only the differences between the two will be described below.

FIG. 5 is a flow chart of steps for judging the optimal depth information of an object in the embodiment of FIG. 4 . In step S150 shown in FIG. 4 , the depth information corresponding to the target is judged according to the optimized 3D depth map, and the focus position of the target is obtained according to the depth information, and sub-steps S151 and S152 are also included. Please refer to FIG. 5 , first, step S151 is executed, and the block depth estimator 151 is used to select a block including the target object, and read the depth information of a plurality of neighboring pixels in the block, and the depth information of these neighboring pixels The depth information is statistically calculated to obtain the optimized depth information of the target. Specifically, the purpose of performing this statistical calculation is to more reliably calculate the effective depth information of the target, so that the possibility of focusing on an incorrect target can be avoided.

For example, the method of performing this statistical operation can be average operation (mean), mode operation (mod), median operation (median), minimum operation (minimum), quartile (quartile) or other suitable Mathematical statistics operation method. In more detail, the average operation refers to using the average depth information of this block as the optimized depth information for the subsequent auto-focus steps, and the majority operation refers to using the depth information with the largest number in this block as the optimized depth information , the median operation is to use the median value of the depth information in this block as the optimized depth information, the minimum value operation is to use the distance of the nearest object in this block as the optimized depth information, and the quartile operation is to use this area The first quartile or the second quartile of the depth information in the block is used as optimized depth information. It is worth noting that the present invention is not limited thereto, and those skilled in the art may select other appropriate statistical calculation methods to obtain the optimized depth information of the target object according to actual needs, so details will not be repeated here.

Next, step S152 is executed to obtain the focus position of the target object according to the optimized depth information. In this embodiment, the method for executing step S152 has been described in detail in the method of step S150 in the embodiment of FIG. 2 , and will not be repeated here. restate.

In addition, please refer to FIG. 4 again. In this embodiment, the auto-focus method further includes executing step S410 , using the object tracking module 153 to execute an object tracking program on the target to obtain at least one characteristic information and motion track of the target. Specifically, the feature information of the target object may include center of gravity, color, area, outline or shape information, and the object tracking module 153 may use different object tracking algorithms to obtain various objects forming the target object in the first and second images. Components, and these components are combined into higher-order feature information, and the target is tracked by comparing the feature information between consecutive first images or second images generated at different time points. It is worth noting that the present invention does not limit the scope of the object tracking algorithm. Those with ordinary knowledge in this technical field can select an appropriate object tracking algorithm according to actual needs to obtain the characteristic information and motion trajectory of the target object. I will not repeat them here. In addition, the object tracking module 153 is also coupled to the block depth estimator 151 , and can feed back its feature information and motion trajectory to the block depth estimator 151 . The block depth estimator 151 can perform statistical calculations of different weighting types according to the feature information of the target object and the pixel reliability (similarity) of the tracking estimation and the depth information of its neighboring pixels, so as to optimize the target object Depth information is more precise.

Fig. 6 is a flow chart of an autofocus method shown in yet another embodiment of the present invention. Referring to FIG. 6 , in this embodiment, the auto-focus method can be implemented by using the auto-focus device 100 in FIG. 1 and the processing unit 150 in FIG. 3 , for example. The autofocus method in this embodiment is similar to the autofocus method in the embodiment in FIG. 4 , and only the differences between the two will be described below.

In this embodiment, the auto-focus method further includes performing step S610 and step S620. In step S610 , the storage unit 140 and the processing unit 150 can be used to store the depth information of the object at different time points in the depth information database 141 (as shown in FIG. 3 ). Specifically, when the autofocus device executes step S150, the three-dimensional position information of the object moving in space can be continuously obtained, so the processing unit 150 can input and store the depth information of the object at different time points to the storage unit 140's depth information database 141.

Next, step S620 is executed, using the movement prediction module 155 to perform movement estimation according to the depth information in the depth information database 141 , so as to obtain the depth variation trend of the target object. Specifically, the movement amount prediction module 155 is coupled to the storage unit 140 and the focusing module 130. When the movement amount prediction module 155 executes the calculation program of the movement amount estimation on the depth information of the depth information database 141, the target objects can be respectively obtained. The change trend of three-dimensional position information moving in space, especially the position change trend of the target along the Z axis, that is, the depth change trend of the target, will help to predict the moving position of the target in the next instant, and therefore Helps with autofocus. Furthermore, after obtaining the depth change trend of the target object, the depth change trend of the target object can be transmitted to the focus module 130, and the focus module 130 can control the first and second image sensors 110, 120 according to Smooth movement of the depth change trend. More specifically, the auto-focus device 100 can pre-adjust the lens positions of the first and second image sensors 110 and 120 according to the depth variation trend of the target before the focusing module 130 executes the auto-focus procedure, so that the first and second image sensors 110 and 120 The lens positions of the second image sensors 110 and 120 may be closer to the focus positions obtained in step S150. In this way, the moving process when the auto-focus device 100 executes the auto-focus procedure in step S160 can be relatively smooth, thereby increasing the stability of the auto-focus device 100 .

In addition, both the depth information database 141 and the movement amount prediction module 155 can also respectively feed back the depth information of the target object at different time points and its depth variation trend to the object tracking module 153, and the object tracking module 153 can also be based on the target object. The depth information and its depth change trend are then calculated and analyzed for characteristic signals and depth information. In this way, the calculation amount of the system can be reduced, the calculation speed can be improved, and the result of object tracking can be made more accurate. Focusing performance of the focusing device 100 .

To sum up, the auto-focus method and auto-focus device of the embodiment of the present invention apply the image processing technology of stereo vision and optimize the three-dimensional depth map generated by it to obtain the focus position, only one image is needed. Time can complete the execution of the relevant auto-focus procedures. Therefore, the auto-focus device and the auto-focus method of the present invention have a fast focusing speed. In addition, because there is no need to search repeatedly, it will not cause breathing phenomenon, and has good focusing stability.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. scope.

Claims (10)

1. An autofocus method is applicable to an autofocus device, characterized in that the autofocus device includes first and second image sensors, and the autofocus method comprises: selecting a target object, and using the first and the second image sensors to photograph the target object to generate a first image and a second image; performing 3D depth estimation according to the first image and the second image to generate a 3D depth map; Optimizing the 3D depth map to generate an optimized 3D depth map; Determining the depth information corresponding to the target object according to the optimized three-dimensional depth map, and obtaining the focus position of the target object according to the depth information; and driving the autofocus device to execute an autofocus program according to the focus position, The steps of judging the depth information corresponding to the target object based on the optimized three-dimensional depth map and obtaining the focus position include: Select a block containing the target object, read the depth information of multiple neighboring pixels in the block, and perform statistical calculations on the depth information of the multiple neighboring pixels to obtain the optimized depth information of the target object ;as well as The in-focus position of the object is obtained according to the optimized depth information. 2. The autofocus method according to claim 1, wherein the step of obtaining the focus position of the target object according to the depth information comprises: The depth comparison table is queried according to the depth information to obtain the focus position of the target. 3. The autofocus method according to claim 1, wherein the method for selecting the target comprises: The auto-focus device receives a user's click signal for selecting the target or the auto-focus device performs a target detection program to automatically select the target and obtain the coordinate position of the target. 4. The autofocus method according to claim 1, further comprising: Executing an object tracking program on the target to obtain at least one feature information and motion track of the target, wherein the feature information includes center of gravity, color, area, outline or shape information. 5. The autofocus method according to claim 1, further comprising: storing the depth information of the target at different time points in a depth information database; and Movement estimation is performed according to the depth information in the depth information database, so as to obtain the depth variation trend of the target object. 6. The autofocus method according to claim 1, wherein the optimization process is Gaussian smoothing process. 7. An autofocus device, characterized in that it comprises: The first and second image sensors are used to photograph the target to generate a first image and a second image; a focus module, controlling the focus positions of the first and the second image sensors; and A processing unit, coupled to the first and second image sensors and the focusing module, the processing unit performs 3D depth estimation on the first image and the second image to generate a 3D depth map, and performs 3D depth estimation on the 3D depth map optimizing processing to generate an optimized three-dimensional depth map, wherein the processing unit further includes: The block depth estimator selects a block including the target object, reads the depth information of a plurality of neighboring pixels in the block, and performs statistical calculations on the depth information of the plurality of neighboring pixels to obtain the Optimal depth information of the target object, and obtaining the focus position of the target object according to the optimized depth information, Wherein the focus module executes an auto-focus procedure according to the focus position. 8. The autofocus device according to claim 7, further comprising: a storage unit, coupled to the processing unit, for storing the first and second images and a depth comparison table, Wherein the processing unit queries the depth comparison table according to the depth information to obtain the focus position of the target. 9. The autofocus device according to claim 7, wherein the processing unit further comprises: An object tracking module, coupled to the block depth estimator, tracks the target to obtain at least one feature information and a motion trajectory, wherein the feature information includes center of gravity, color, area, outline or shape information, so that the block depth The estimator performs the statistical calculation according to the at least one feature information and the depth information of the plurality of neighboring pixels. 10. The autofocus device according to claim 8, wherein the storage unit further comprises a depth information database for storing the depth information of the target at different time points, and the processing unit further comprises: The movement amount prediction module is coupled to the storage unit and the focus module, and performs movement amount prediction according to the depth information in the depth information database to obtain the depth change trend of the target object, so that the focus module can control the first One and the second image sensor move smoothly according to the depth variation trend.