CN111368651B - Vehicle identification method and device and electronic equipment - Google Patents
Vehicle identification method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application provides a vehicle identification method, a vehicle identification device and electronic equipment. The method comprises the following steps: acquiring vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model features obtained after modeling a shot picture or shot video of the vehicle to be identified; and if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle, determining that the vehicle to be identified is the target vehicle. By adopting the technical scheme provided by the embodiment of the application, the vehicle identification of the vehicle which cannot acquire the license plate number can be realized.
Description
Technical Field
The present disclosure relates to the field of intelligent transportation technologies, and in particular, to a vehicle identification method, device and electronic equipment.
Background
In some application scenarios, it is required to determine whether a vehicle to be identified is a target vehicle, for example, in vehicle monitoring, a monitoring video including each vehicle traveling on a road may be acquired by a video acquisition device provided in the road, and then the target vehicle is identified in each vehicle included in the monitoring video, so that the target vehicle is subjected to monitoring processing.
In the related art, the flow of vehicle identification may be: the server may be preset with a license plate number of the target vehicle. The server may acquire the license plate number of the vehicle to be identified, then the server may compare the license plate number with the license plate number of the target vehicle, and if the two vehicle numbers are the same, the server may determine the vehicle to be identified as the target vehicle.
However, in some application scenarios, the server cannot obtain the license plate number of the vehicle to be identified, for example, vehicle monitoring, and may not identify the license plate number of the vehicle to be identified from the monitoring video due to the license plate of the vehicle to be identified being blocked. So that it cannot be determined whether the vehicle to be identified is a target vehicle.
Disclosure of Invention
An object of the embodiment of the application is to provide a vehicle identification method, a device and electronic equipment, so as to realize vehicle identification on a vehicle which cannot acquire a license plate number. The specific technical scheme is as follows:
in a first aspect of the present invention, there is provided a vehicle identification method, the method comprising:
acquiring vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model features obtained after modeling a shot picture or shot video of the vehicle to be identified;
And if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle, determining that the vehicle to be identified is the target vehicle.
In a possible embodiment, the vehicle information further includes other vehicle features besides the vehicle model feature, where the other vehicle features are vehicle features obtained after modeling a captured picture or a captured video of the vehicle to be identified; before the determining that the vehicle model feature of the vehicle to be identified and the pre-stored vehicle model feature of the target vehicle match, the method further comprises:
and determining the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle as a preset comparison result.
In one possible embodiment, the other vehicle features include at least two;
the determining that the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle is a preset comparison result comprises:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristic of the target vehicle stored in advance according to the sequence from high to low of the preset priority of each other vehicle characteristic;
And determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; the preset priority of the vehicle brands is higher than the preset priority of the vehicle sub-brands, the preset priority of the vehicle sub-brands is higher than the preset priority of the vehicle styles, and the preset priority of the vehicle styles is higher than the preset priority of the vehicle colors;
the preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
In a second aspect of the present invention, there is provided a vehicle identification apparatus, the apparatus comprising:
the vehicle identification system comprises an acquisition module, a detection module and a storage module, wherein the acquisition module is used for acquiring vehicle information of a vehicle to be identified, and the vehicle information comprises vehicle model characteristics obtained after modeling a shooting picture or shooting video of the vehicle to be identified;
And the matching module is used for determining the vehicle to be identified as the target vehicle if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle.
In a possible embodiment, the vehicle information further includes other vehicle features besides the vehicle model feature, where the other vehicle features are vehicle features obtained after modeling a captured picture or a captured video of the vehicle to be identified; the apparatus further comprises:
and the comparison module is used for determining the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle as a preset comparison result.
In one possible embodiment, the other vehicle features include at least two;
the comparison module is specifically configured to sequentially compare each other vehicle feature of the vehicle to be identified with the same other vehicle feature of the target vehicle stored in advance according to the order of the preset priority of each other vehicle feature from high to low;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; and the preset priority of the vehicle brand is higher than the preset priority of the vehicle sub-brand, the preset priority of the vehicle sub-brand is higher than the preset priority of the vehicle style, and the preset priority of the vehicle style is higher than the preset priority of the vehicle color
The preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
In a third aspect of the present invention, there is provided an electronic apparatus comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any of the above first aspects when executing a program stored on a memory.
In a fourth aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method steps of any of the first aspects described above.
According to the vehicle identification method, the device and the electronic equipment, the similarity degree of the appearance of the vehicle to be identified and the appearance of the target vehicle can be determined by matching the vehicle model characteristics of the vehicle to be identified and the vehicle model characteristics of the target vehicle, and the appearance of the vehicle is not obviously changed generally, so that whether the vehicle to be identified is the target vehicle can be judged through the similarity degree, and further vehicle identification on the vehicle which cannot acquire the license plate number can be realized.
Of course, not all of the above-described advantages need be achieved simultaneously in practicing any one of the products or methods of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a vehicle identification method provided in an embodiment of the present application;
fig. 2 is a flowchart of a vehicle identification method according to an embodiment of the present application;
FIG. 3 is a flowchart of a vehicle monitoring method according to an embodiment of the present application;
fig. 4a is a schematic structural diagram of a vehicle identification device according to an embodiment of the present application;
fig. 4b is a schematic structural diagram of another vehicle identification device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a vehicle identification method, which can be applied to electronic equipment with a vehicle identification function, wherein in a possible application scene, the electronic equipment can be a server for monitoring, and the server can be electronic equipment with a data processing function based on real-time stream calculation, such as a computer, a tablet computer and the like. In this application scenario, the server may perform vehicle monitoring based on a stream computation framework, such as SparkStreaming, boom. In the embodiment of the application, the server may perform vehicle monitoring based on SparkStreaming, kafka (distributed publish-subscribe message system) and Redis (Redis database).
In the process of monitoring the vehicles, the server can acquire a monitoring video containing each vehicle running on the road through a video acquisition device arranged on the road, then the server can take each vehicle in the monitoring video as a vehicle to be identified, and then the server can identify the license plate number of the vehicle to be identified through a preset image identification algorithm. If the license plate number of the vehicle to be identified is identified, the server can compare the license plate number with the license plate number of the target vehicle stored in advance; if the two license plate numbers are the same, the server can determine the vehicle to be identified as a target vehicle; if the two license plate numbers are different, the server can not perform subsequent processing. If the server cannot identify the license plate number of the vehicle to be identified, the server can judge whether the vehicle to be identified, which cannot acquire the license plate number, is a target vehicle or not through the vehicle identification method provided by the embodiment of the application.
It can be understood that the application scenario is only one possible application scenario of the vehicle identification method provided by the embodiment of the present invention, and the vehicle identification method provided by the embodiment of the present invention can also be applied to other application scenarios.
As shown in fig. 1, a specific processing procedure of the vehicle identification method provided in the embodiment of the present application includes:
step 101, acquiring vehicle information of a vehicle to be identified.
The vehicle to be identified is a vehicle which needs to determine whether the vehicle is a target vehicle, and taking vehicle monitoring as an example, the vehicle to be identified can be each vehicle in the monitoring video. In other possible application scenarios, the vehicle to be identified may also be a vehicle in a historical driving image or a historical driving video.
The vehicle information includes vehicle model features obtained after modeling a photographed picture or video of a vehicle to be identified, the vehicle model features being used to represent the appearance of the vehicle, which may refer to a visual effect when the vehicle is viewed from the outside of the vehicle, it being understood that the vehicle model features may also represent a part of interior decoration in the vehicle due to the presence of a light-transmissive window in the vehicle.
Taking a server for vehicle monitoring as an example, a modeling analysis algorithm, such as OpenCV (Open source Computer Vision library ), openGL (Open Graphics Library, open graphics library), may be preset in the server. In the related art, any algorithm with a function of performing three-dimensional reconstruction based on video to obtain the features of the vehicle model of the target object in the video may be used as the modeling analysis algorithm, and the embodiment of the application is not particularly limited.
In an example, the server may reconstruct, for each vehicle to be identified in the monitoring video, the vehicle to be identified in three dimensions through a modeling analysis algorithm and the monitoring video, to obtain a vehicle model feature of the vehicle to be identified, as vehicle information of the vehicle to be identified.
In one possible implementation manner, the server may generate, for each vehicle to be identified included in the monitoring video, a monitoring number of the vehicle to be identified, and then the server may store the monitoring number and a vehicle model feature of the vehicle to be identified correspondingly, to obtain vehicle information of the vehicle to be identified.
Step 102, if it is determined that the vehicle model feature of the vehicle to be identified matches the vehicle model feature of the pre-stored target vehicle, determining that the vehicle to be identified is the target vehicle.
In one possible implementation, a pre-stored vehicle model feature of the target vehicle may be obtained, then a similarity of the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle may be calculated, and then the similarity may be compared with a preset similarity threshold. If the similarity is greater than a preset similarity threshold, the vehicle model features of the vehicle to be identified and the vehicle model features of the target vehicle can be considered to be matched, and then the vehicle to be identified is determined to be the target vehicle. In one possible embodiment, if the similarity is smaller than the preset similarity threshold, the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle may be considered to be mismatched, and further, no subsequent processing may be performed.
For example, the similarity between the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle may be calculated, the obtained similarity is assumed to be 90%, and then the similarity is compared with a preset similarity threshold value of 80%, and since the similarity 90% is greater than the preset similarity threshold value of 80%, it may be determined that the vehicle model feature and the vehicle model feature of the target vehicle match, and thus it is determined that the vehicle to be identified is the target vehicle.
In this embodiment of the present application, when the number of target vehicles is plural, it may be determined, for each target vehicle, whether the vehicle model feature of the vehicle to be identified matches the vehicle model feature of the target vehicle. The matching modes may be the same or different for different target vehicles, and this embodiment is not limited thereto.
It is understood that the vehicle model feature may represent the appearance of the vehicle, and thus if the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle match, the appearance of the vehicle to be identified may be considered to be more similar to the appearance of the target vehicle. The appearance of one vehicle is not obviously changed, and the appearances of different vehicles are different to some extent, so that if the appearance of the vehicle to be identified is similar to the appearance of the target vehicle to a higher degree, the vehicle to be identified can be considered as the target vehicle.
When the target vehicle is a plurality of vehicles, it may be determined that the vehicle to be identified is a vehicle, among the plurality of target vehicles, that matches the vehicle model feature of the vehicle to be identified. For example, assuming that a total of 3 target vehicles are included, respectively denoted as target vehicles 1-3, assuming that the vehicle model features of the vehicle to be identified match the vehicle model features of the target vehicle 2 and do not match the vehicle model features of the target vehicles 1, 3, the vehicle to be identified is determined to be the target vehicle 2.
In the embodiment of the application, the similarity degree of the appearance of the vehicle to be identified and the appearance of the target vehicle can be determined by matching the vehicle model characteristics of the vehicle to be identified and the vehicle model characteristics of the target vehicle, and the appearance of the vehicle generally does not change obviously, so that whether the vehicle to be identified is the target vehicle can be judged through the similarity degree, and further the vehicle identification on the vehicle which cannot acquire the license plate number can be realized.
Optionally, the vehicle information may further include other vehicle features in addition to the vehicle model features, and the other vehicle features may include at least one of a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color. Wherein the vehicle brand is e.g. BMW, magda; sub-brands of vehicles such as, for example, huachenbao horse, yigaoda; vehicle styles such as sedan, off-road vehicle; vehicle colors such as black, silver. The other vehicle features may be vehicle features obtained after modeling a captured picture or captured video of the vehicle to be identified.
After the above-described vehicle information of the vehicle to be identified is acquired, the vehicle information of the corresponding kind of the target vehicle may be acquired. Then, before determining whether the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle match, the vehicle information of the vehicle to be identified and the vehicle information of the target vehicle may be compared, and whether the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle match may be further determined according to the comparison result.
The specific treatment process comprises the following steps:
and step 1, acquiring vehicle information of the vehicle to be identified.
In implementation, the specific processing procedure of this step is the same as that of step 101, and will not be described here again.
And 2, determining the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle as a preset comparison result.
In one possible embodiment, if it is determined that the comparison result is different from the preset comparison result, no subsequent processing may be performed.
In the embodiment of the present application, the manner of comparing the other vehicle characteristics of the vehicle to be identified with the other vehicle characteristics of the target vehicle is different for different types of the acquired vehicle information. When there are other vehicle features including at least two, it may be that each other vehicle feature of the vehicle to be identified and the same kind of other vehicle feature of the target vehicle are compared in sequence. It is also possible to compare in parallel each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristic of the target vehicle.
For example, when one other vehicle feature of the vehicle to be identified is acquired, the one other vehicle feature of the vehicle to be identified and the one other vehicle feature of the target vehicle may be directly compared to obtain a comparison result; when at least two other vehicle characteristics of the vehicle to be identified are obtained, comparing priorities can be compared according to preset other vehicle characteristics, and each other vehicle characteristic of the vehicle to be identified and the same kind of other vehicle characteristic of the target vehicle can be compared in sequence to obtain a comparison result. The other vehicle feature comparison priorities may be in order of high to low: vehicle brand, vehicle sub-brand, vehicle style, vehicle color. In other possible embodiments, the comparison priority of each other vehicle feature may also be different according to different actual requirements, and the specific form of the comparison priority of the vehicle information in the embodiment of the present application is not limited.
The preset comparison results are different according to the different types of the other vehicle characteristics of the acquired vehicles to be identified. For example, when the other vehicle characteristic includes only one vehicle characteristic, the preset comparison result may be that the other vehicle characteristic of the vehicle to be identified and the other vehicle characteristic of the target vehicle are the same. When the other vehicle features include at least two vehicle features, the preset comparison result may be set according to actual requirements.
For example, in one possible embodiment, the preset comparison may be the same for each of the vehicle features included with the other vehicle features. In another possible embodiment, the preset comparison result may be that the number of types of the same other vehicle features is greater than or equal to (or greater than in other possible embodiments) a preset type threshold, where the preset type threshold may be set according to actual requirements, for example, if the other vehicle features include 5 types and the preset type threshold is 3 types, it may be determined that the comparison result is the preset comparison result when the vehicle to be identified and the target vehicle have three or more types of the same other vehicle features. In another possible embodiment, the preset weight may be set for each other vehicle feature, the preset comparison result may be that the sum of the preset weights of the same other vehicle feature is equal to or greater than (or greater than in other possible embodiments) a preset numerical threshold, the preset numerical threshold may be set according to the actual requirement, for example, assuming that the preset numerical threshold is 3, the vehicle to be identified has the same vehicle brand, the vehicle sub-brand, and the vehicle color as the target vehicle, and assuming that the preset weight of the vehicle brand is 1, the preset weight of the vehicle sub-brand is 1.2, and the preset weight of the vehicle color is 0.8, then the comparison result may be determined to be the preset comparison result because the sum of the preset weights of the same other vehicle feature is equal to 3. In other possible embodiments, the preset comparison result may be in other forms, which is not limited in this embodiment.
Step 3, if the vehicle model feature of the vehicle to be identified is determined to be matched with the pre-stored vehicle model feature of the target vehicle, determining the vehicle to be identified as the target vehicle
This step is the same as step 102 described above, and reference may be made to the description of step S102 described above, which is not repeated here.
As described in connection with step 2 above, it will be appreciated that depending on the setting of the preset comparison result, in some possible embodiments, it may not be necessary to complete a comparison of each other vehicle feature of the vehicle to be identified with the same other vehicle feature of the target vehicle, in order to determine whether the comparison result is the preset comparison result. For example, assuming that the other vehicle features include 5 kinds, the number of kinds of other vehicle features whose preset target comparison result is the same is equal to or greater, if the three other vehicle features of the vehicle to be recognized and the target vehicle are the same after the comparison of the three other vehicle features is completed, it may be determined whether the remaining two other vehicle features of the vehicle to be recognized and the target vehicle are the same or not without comparing any more, and the comparison result is the preset comparison result.
For another example, assuming that the other vehicle characteristic information includes a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color, the following steps may be specifically included as shown in fig. 2:
In step 201, it is determined whether the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle.
In practice, the vehicle brand of the vehicle to be identified may be compared with the vehicle brand of the target vehicle, and if the two vehicle brands are the same, step 202 may be performed; if the two vehicle brands are different, step 206 may be performed.
In the embodiment of the present application, the manner of determining whether the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle may be various. In one possible implementation, the field representing the vehicle brand of the vehicle to be identified may be directly compared with the field of the vehicle brand of the target vehicle, and if the two fields are identical, it may be determined that the vehicle brand of the vehicle to be identified is identical to the vehicle brand of the target vehicle.
In another possible implementation, a field representing the vehicle brand of the vehicle to be identified may also be calculated, and the similarity to a field representing the vehicle brand of the target vehicle may then be compared to a preset field similarity threshold. If the similarity is greater than the field similarity threshold, it may be determined that the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle. If the similarity is less than the field similarity threshold, it may be determined that the vehicle brand of the vehicle to be identified is different from the vehicle brand of the target vehicle.
For example, a field "mazda" indicating the vehicle brand of the vehicle to be identified may be calculated, and a similarity with a field "maha" indicating the vehicle brand of the target vehicle may be obtained by 100%, and then the similarity may be compared with a preset field similarity threshold value of 90%, and since the similarity 100% is greater than the similarity threshold value of 90%, it may be determined that the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle.
Step 202, determining whether a vehicle sub-brand of the vehicle to be identified is identical to a vehicle sub-brand of the target vehicle.
In practice, it may be determined whether the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle. If the two vehicle sub-brands are the same, then step 203 may be performed; if the two vehicle sub-brands are not identical, step 206 may be performed.
Step 203, it is determined whether the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle.
In practice, it may be determined whether the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle. If the two vehicle styles are the same, step 204 may be performed; if the two vehicle styles are not the same, step 206 may be performed.
Step 204, it is determined whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle.
In practice, it may be determined whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle. If the two vehicles are the same color, step 205 may be performed; if the two vehicles are not of the same color, step 206 may be performed.
Step 205 is performed to determine whether the vehicle model features of the vehicle to be identified and the pre-stored vehicle model features of the target vehicle match.
In implementation, the specific processing procedure of this step is similar to that of step 102, and will not be described here again.
Step 206, no subsequent processing is performed.
In this embodiment of the present invention, the obtained other vehicle features of the vehicle to be identified may be at least one of a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color, and accordingly, when different types of other vehicle features of the vehicle to be identified are obtained, the specific processing procedure for comparing each other vehicle feature of the vehicle to be identified with other vehicle features of the target vehicle is also different according to the comparison priority of the other vehicle feature information. The process of comparing the other vehicle characteristics of the vehicle to be identified with those of the target vehicle for different types of other vehicle characteristics is similar to steps 201 to 206, and will not be repeated here.
In the embodiment of the application, vehicle information such as a vehicle brand, a vehicle sub-brand, a vehicle style, a vehicle color and the like of the vehicle to be identified can also be obtained. Before matching the vehicle model feature of the vehicle to be identified with the vehicle model feature of the target vehicle, other vehicle features of the vehicle to be identified may be sequentially compared with other vehicle features of the target vehicle of the corresponding kind. Therefore, based on the comparison of various vehicle characteristics of the target vehicle and the vehicle to be identified, whether the vehicle to be identified and the target vehicle are the same vehicle or not can be determined based on various dimensions, and the accuracy of the comparison of the vehicle to be identified and the target vehicle is improved. Meanwhile, whether the vehicle to be identified and the target vehicle are the same vehicle or not is judged based on the correlation degree of other observable vehicle characteristics, the vehicle to be identified with low correlation degree with the target vehicle can be removed to the maximum extent, and the calculation resources required for vehicle identification are reduced.
Alternatively, a vehicle information filtering condition for filtering the vehicle information that does not include the vehicle model feature may be preset. After the vehicle information of the vehicle to be identified is acquired, whether the vehicle information meets the vehicle information filtering condition or not can be judged through the vehicle information filtering condition and the vehicle information, namely whether modeling analysis on the vehicle to be identified is successful or not is judged, and whether the vehicle model characteristics of the vehicle to be identified are obtained or not is judged.
And if the vehicle information of the vehicle to be identified does not meet the preset vehicle information filtering condition, executing a similarity step of determining the vehicle model characteristics of the vehicle to be identified and the pre-stored vehicle model characteristics of the target vehicle.
In implementation, if the vehicle information of the vehicle to be identified does not meet the preset vehicle information filtering condition, it may be determined that the vehicle model feature of the vehicle to be identified is successfully acquired, and then step 102 may be performed to determine whether the vehicle to be identified and the target vehicle are the same vehicle.
If the vehicle information of the vehicle to be identified meets the preset vehicle information filtering condition, the subsequent processing is not required.
In the embodiment of the application, whether the vehicle information of the vehicle to be identified meets the preset vehicle information filtering condition can be judged, so that whether the vehicle information of the vehicle to be identified contains the vehicle model feature of the vehicle to be identified is judged, and the similarity of the vehicle model feature of the vehicle to be identified and the vehicle model feature of the target vehicle is conveniently calculated. Therefore, the comparison of the vehicle to be identified and the target vehicle based on invalid vehicle information can be avoided, the calculation resources required for vehicle identification are reduced, and the vehicle identification efficiency is improved.
In a possible application scenario, the vehicle identification method provided by the embodiment of the invention can be applied to vehicle monitoring based on the stream processing framework, so that the server for vehicle monitoring can store the vehicle information of the target vehicle in a local database, and the server can also store the vehicle information of the target vehicle in a non-relational database. Wherein the non-relational database is, for example, a Redis database, an Hbase database.
When an update instruction of the vehicle information of the target vehicle is received, the server adds or deletes the locally stored vehicle information of the target vehicle, but does not change the vehicle information of the target vehicle in the non-relational database, so when the vehicle information of the target vehicle is stored in the non-relational database, the server needs to determine whether the vehicle information of the target vehicle is updated or not before acquiring the vehicle information of the target vehicle. As shown in fig. 3, the method specifically comprises the following steps:
step 301, it is determined whether the vehicle information of the target vehicle in the non-relational database needs to be updated.
In an implementation, if the vehicle information of the target vehicle is stored in a non-relational database, the server may obtain an update record of the database, in which an update time of the vehicle information in the database may be recorded.
The server may compare the latest update time of the vehicle information in the update record with the update time of the stored vehicle information in the non-relational database. If the latest update time is the same as the update time, the server may determine that it is not necessary to update the vehicle information of the target vehicle in the non-relational database; if the latest update time is greater than the update time, the server may determine that the vehicle information of the target vehicle in the non-relational database needs to be updated.
Step 302, if the vehicle information of the target vehicle in the non-relational database needs to be updated, acquiring the updated vehicle information of the target vehicle.
In an implementation, if the vehicle information of the target vehicle in the non-relational database needs to be updated, the server may acquire the vehicle information in the database and store the acquired vehicle information in the non-relational database as the updated vehicle information of the target vehicle.
In this embodiment of the present application, when storing the vehicle information of the target vehicle based on the Redis database, the server may create a monitoring procedure for the database through SparkStreaming, and before acquiring the vehicle information of the target vehicle, the server may acquire the update identifier of the database through the monitoring procedure. If the update identification indicates that the database was updated again after the last update of the non-relational database, the server may determine that the vehicle information of the target vehicle in the non-relational database needs to be updated. Then, the server may acquire the vehicle information in the database and store the acquired vehicle information in the Redis database.
In this embodiment of the present application, if the vehicle information of the target vehicle is stored in the non-relational database in a cached form, the server may determine whether the vehicle information of the target vehicle in the non-relational database needs to be updated, and if the vehicle information of the target vehicle in the non-relational database needs to be updated, the server may obtain the updated vehicle information of the target vehicle. The updated vehicle information of the target vehicle is conveniently compared with the vehicle information of the vehicle to be identified, and the timeliness and accuracy of vehicle monitoring are improved.
Optionally, after determining that the vehicle to be identified is the target vehicle, the vehicle information of the vehicle to be identified may be recorded, where the recording may be: and acquiring the vehicle identification of the vehicle to be identified, and correspondingly storing the vehicle identification of the vehicle to be identified and the vehicle information of the vehicle to be identified.
In implementation, a vehicle number of the vehicle to be identified may be acquired as a vehicle identification of the vehicle to be identified, and then the vehicle identification of the vehicle to be identified and the vehicle information of the vehicle to be identified may be stored correspondingly.
In this embodiment of the present invention, various ways may be used to record the vehicle information of the vehicle to be identified, and in one possible implementation manner, a preset special identifier may be added to the vehicle information of the vehicle to be identified, so as to mark the vehicle information as the detected vehicle information of the target vehicle. For example, a special mark "110" is added to the vehicle information of the vehicle to be identified.
In another possible implementation, when the vehicle information of the vehicle to be identified is matched with the vehicle information of the multi-item target vehicle, the vehicle number 001 of the vehicle to be identified may be saved in a preset vehicle blacklist, and a new vehicle number 001-002 may be generated. Therefore, the vehicle to be identified can be conveniently determined to be the target vehicle according to the vehicle number of the vehicle to be identified.
In the embodiment of the application, the vehicle identifier of the vehicle to be identified can be obtained, and the vehicle identifier of the vehicle to be identified and the vehicle information of the vehicle to be identified are correspondingly stored. Therefore, the method and the device are convenient for acquiring the monitoring video containing the vehicle to be identified based on the vehicle information of the vehicle to be identified, so that the target vehicle is monitored.
The embodiment of the application also provides an implementation manner of the server for monitoring the vehicle based on sparks streaming, and the specific processing procedure comprises the following steps: the server can read the vehicle information of the target vehicle from a local database and cache the vehicle information of the target vehicle in a Redis database; then, the server can establish a monitoring flow aiming at the database through sparks streaming so as to acquire the update identification of the database through the monitoring flow, and whether the vehicle information of the target vehicle in the Redis database needs to be updated or not is conveniently judged according to the update identification.
Meanwhile, the server may acquire the vehicle information of the vehicle to be identified from kafka through sparks streaming. Since the vehicle information of the vehicle to be identified acquired by the server through the kafka may contain vehicle information which is not subjected to modeling analysis, or the vehicle information of the vehicle model feature is not obtained due to failure of modeling analysis on the vehicle appearance feature, the server may convert the vehicle information of the vehicle to be identified into a map object, delete the vehicle information after non-modeling analysis, and obtain the vehicle information of the vehicle model feature after the modeling analysis through a preset filter (filtering) operator, vehicle information filtering conditions, and vehicle information.
Then, the server can acquire the updated vehicle information of the target vehicle in the Redis database through sparks streaming, and compare the vehicle information of the vehicle to be identified, which does not meet the vehicle information filtering condition, with the updated vehicle information of the target vehicle in the Redis database one by one according to the sequence of the vehicle brand, the vehicle sub-brand, the vehicle style, the vehicle color and the vehicle model characteristics. If the comparison result is that the vehicle to be identified is the target vehicle, the server can store the vehicle information of the vehicle to be identified.
Thereafter, the server may correspondingly store the vehicle information of the vehicle to be identified to generate a vehicle information set of the target vehicle. The server may send the set of vehicle information into the topic of kafka.
The embodiment of the application also provides a vehicle identification device, as shown in fig. 4a, the device comprises:
an obtaining module 410, configured to obtain vehicle information of a vehicle to be identified, where the vehicle information includes vehicle model features obtained after modeling a shot picture or a shot video of the vehicle to be identified;
and the matching module 420 is configured to determine that the vehicle to be identified is the target vehicle if it is determined that the vehicle model feature of the vehicle to be identified matches the vehicle model feature of the pre-stored target vehicle.
In a possible embodiment, as shown in fig. 4b, the vehicle information further includes other vehicle features besides the vehicle model feature, where the other vehicle features are vehicle features obtained by modeling a photographed picture or a photographed video of the vehicle to be identified; the apparatus further comprises:
the comparison module 430 is configured to determine a comparison result of the other vehicle characteristics of the vehicle to be identified and other vehicle characteristics of a pre-stored target vehicle as a preset comparison result.
In one possible embodiment, the other vehicle features include at least two;
the comparison module is specifically configured to sequentially compare each other vehicle feature of the vehicle to be identified with the same other vehicle feature of the target vehicle stored in advance according to the order of the preset priority of each other vehicle feature from high to low;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; and the preset priority of the vehicle brand is higher than the preset priority of the vehicle sub-brand, the preset priority of the vehicle sub-brand is higher than the preset priority of the vehicle style, and the preset priority of the vehicle style is higher than the preset priority of the vehicle color
The preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
The embodiment of the application also provides an electronic device, as shown in fig. 5, including:
a memory 501 for storing a computer program;
the processor 502 is configured to implement the following steps of a vehicle identification method when executing a program stored in the memory 501:
acquiring vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model features obtained after modeling a shot picture or shot video of the vehicle to be identified;
and if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle, determining that the vehicle to be identified is the target vehicle.
In a possible embodiment, the vehicle information further includes other vehicle features besides the vehicle model feature, where the other vehicle features are vehicle features obtained after modeling a captured picture or a captured video of the vehicle to be identified; before the determining that the vehicle model feature of the vehicle to be identified and the pre-stored vehicle model feature of the target vehicle match, the method further comprises:
and determining the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle as a preset comparison result.
In one possible embodiment, the other vehicle features include at least two;
the determining that the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle is a preset comparison result comprises:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristic of the target vehicle stored in advance according to the sequence from high to low of the preset priority of each other vehicle characteristic;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; the preset priority of the vehicle brands is higher than the preset priority of the vehicle sub-brands, the preset priority of the vehicle sub-brands is higher than the preset priority of the vehicle styles, and the preset priority of the vehicle styles is higher than the preset priority of the vehicle colors;
the preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
The Memory mentioned in the electronic device may include a random access Memory (Random Access Memory, RAM) or may include a Non-Volatile Memory (NVM), such as at least one magnetic disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In yet another embodiment provided herein, there is also provided a computer readable storage medium having stored therein a computer program which when executed by a processor implements the steps of any of the vehicle identification methods described above.
In yet another embodiment provided herein, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the vehicle identification methods of the above embodiments.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, tape), an optical medium (e.g., DVD), or other medium (e.g., solid State Disk (SSD)), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application. Any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (6)
1. A vehicle identification method applied to an electronic device having a data processing function based on real-time stream calculation, the method comprising:
acquiring vehicle information of a vehicle to be identified from Kafka, wherein the vehicle information comprises vehicle model features obtained after modeling a shot picture or shot video of the vehicle to be identified;
if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of a pre-stored target vehicle, determining that the vehicle to be identified is the target vehicle;
the vehicle information further comprises other vehicle characteristics besides the vehicle model characteristics, wherein the other vehicle characteristics are obtained by modeling a shot picture or a shot video of the vehicle to be identified; before the determining that the vehicle model feature of the vehicle to be identified and the pre-stored vehicle model feature of the target vehicle match, the method further comprises:
Determining the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle as a preset comparison result;
the other vehicle features include at least two;
the determining that the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle is a preset comparison result comprises:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristic of the target vehicle stored in advance according to the sequence from high to low of the preset priority of each other vehicle characteristic;
determining the comparison results of all the other vehicle characteristics as preset comparison results; the preset comparison result is that the sum of preset weights of the same other vehicle features is larger than or equal to a preset numerical threshold, wherein the preset weights of different other vehicle features are different;
the determining that the comparison result of all the other vehicle features is the preset comparison result includes:
if the comparison result obtained after the comparison of the other vehicle features of the current priority is determined to be finished meets the preset comparison result, the remaining other vehicle features of the vehicle to be identified and the target vehicle are not compared; the remaining other vehicle features are the other vehicle features of the priority level located after the current priority level in the order of the preset priority level from high to low;
Before the step of determining that the vehicle to be identified is the target vehicle if the vehicle model feature of the vehicle to be identified and the vehicle model feature of the pre-stored target vehicle are determined to match, the method further includes:
determining that the vehicle information of the vehicle to be identified does not meet a preset vehicle information filtering condition, wherein the vehicle information filtering condition is that the vehicle information does not contain vehicle model characteristics;
the vehicle information of the target vehicle is stored in a non-relational database in advance; the method further comprises the steps of:
creating a monitoring flow for the non-relational database;
acquiring an update identifier of the non-relational database according to the monitoring flow;
and if the update identifier indicates that the database is updated again after the non-relational database is updated last time, acquiring the updated vehicle information of the target vehicle, and storing the updated vehicle information of the target vehicle into the non-relational database.
2. The method of claim 1, wherein the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; the preset priority of the vehicle brands is higher than the preset priority of the vehicle sub-brands, the preset priority of the vehicle sub-brands is higher than the preset priority of the vehicle styles, and the preset priority of the vehicle styles is higher than the preset priority of the vehicle colors;
The preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
3. A vehicle identification apparatus, characterized by being applied to an electronic device having a data processing function based on real-time stream calculation, comprising:
the vehicle information comprises vehicle model features obtained after modeling a shot picture or shot video of the vehicle to be identified;
the matching module is used for determining the vehicle to be identified as the target vehicle if the vehicle model characteristics of the vehicle to be identified are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle;
the vehicle information further comprises other vehicle characteristics besides the vehicle model characteristics, wherein the other vehicle characteristics are obtained by modeling a shot picture or a shot video of the vehicle to be identified; the apparatus further comprises:
The comparison module is used for determining that the comparison result of the other vehicle characteristics of the vehicle to be identified and the other vehicle characteristics of the pre-stored target vehicle is a preset comparison result;
the other vehicle features include at least two;
the comparison module is specifically configured to sequentially compare each other vehicle feature of the vehicle to be identified with the same other vehicle feature of the target vehicle stored in advance according to the order of the preset priority of each other vehicle feature from high to low;
determining the comparison results of all the other vehicle characteristics as preset comparison results; the preset comparison result is that the sum of preset weights of the same other vehicle features is larger than or equal to a preset numerical threshold, wherein the preset weights of different other vehicle features are different;
the determining that the comparison result of all the other vehicle features is the preset comparison result includes:
if the comparison result obtained after the comparison of the other vehicle features of the current priority is determined to be finished meets the preset comparison result, the remaining other vehicle features of the vehicle to be identified and the target vehicle are not compared; the remaining other vehicle features are the other vehicle features of the priority level located after the current priority level in the order of the preset priority level from high to low;
The apparatus further comprises:
the filtering module is used for determining that the vehicle information of the vehicle to be identified does not meet the preset vehicle information filtering condition before the step of determining that the vehicle to be identified is the target vehicle if the vehicle model characteristics of the vehicle to be identified are matched with the pre-stored vehicle model characteristics of the target vehicle, and the vehicle information filtering condition is that the vehicle information does not contain the vehicle model characteristics;
the vehicle information of the target vehicle is stored in a non-relational database in advance; the apparatus further comprises:
the creation module is used for creating a monitoring flow aiming at the non-relational database;
the update identification acquisition module is used for acquiring the update identification of the non-relational database according to the monitoring flow;
and the updating module is used for acquiring the updated vehicle information of the target vehicle and storing the updated vehicle information of the target vehicle into the non-relational database if the updating identifier indicates that the database is updated again after the non-relational database is updated last time.
4. The apparatus of claim 3, wherein the other vehicle features include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; the preset priority of the vehicle brands is higher than the preset priority of the vehicle sub-brands, the preset priority of the vehicle sub-brands is higher than the preset priority of the vehicle styles, and the preset priority of the vehicle styles is higher than the preset priority of the vehicle colors;
The preset comparison result comprises:
the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle, the vehicle sub-brand of the vehicle to be identified is the same as the vehicle sub-brand of the target vehicle, the vehicle style of the vehicle to be identified is the same as the vehicle style of the target vehicle, and whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle or not.
5. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-2 when executing a program stored on a memory.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-2.
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