CN111368651A - 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 and device and electronic equipment. The method comprises the following steps: the method comprises the steps of obtaining vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified; and if the vehicle model features of the vehicle to be recognized are determined to be matched with the vehicle model features of the pre-stored target vehicle, determining that the vehicle to be recognized 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 application relates to the field of intelligent transportation technologies, and in particular, to a vehicle identification method and apparatus, and an electronic device.
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 vehicles running on a road may be acquired by a video capture device disposed in the road, and then the target vehicle is identified from the vehicles included in the monitoring video, so as to perform monitoring processing such as positioning, tracking, and track checking on the target vehicle.
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 can acquire the license plate number of the vehicle to be recognized, then the server can 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 can determine the vehicle to be recognized as the target vehicle.
However, in some application scenarios, the server cannot acquire the license plate number of the vehicle to be recognized, and for example, in the case of vehicle monitoring, the license plate number of the vehicle to be recognized may be blocked, so that the server cannot recognize the license plate number of the vehicle to be recognized from the monitoring video. So that it is impossible to determine whether the vehicle to be identified is the target vehicle.
Disclosure of Invention
The embodiment of the application aims to provide a vehicle identification method, a vehicle identification device and electronic equipment, so that vehicle identification of a vehicle which cannot acquire a license plate number is realized. 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:
the method comprises the steps of obtaining vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified;
and if the vehicle model features of the vehicle to be recognized are determined to be matched with the vehicle model features of the pre-stored target vehicle, determining that the vehicle to be recognized 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 shot picture or a shot video of the vehicle to be identified; before the determining that the vehicle model features of the vehicle to be identified match the vehicle model features of the pre-stored target vehicle, 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 between 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 includes:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristics of the pre-stored target vehicle according to the sequence of the preset priority of each other vehicle characteristic 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 characteristics include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; 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 is determined.
In a second aspect of the present invention, there is provided a vehicle identification device, the device comprising:
the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified;
and the matching module is used for determining that the vehicle to be identified is 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 shot picture or a shot video of the vehicle to be identified; the device further comprises:
and 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.
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 kind of other vehicle features of the pre-stored target vehicle in an order from a high priority to a low priority of each other vehicle feature;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle characteristics include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; 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 is determined.
In a third aspect of the present invention, there is provided an electronic device comprising:
a memory for storing a computer program;
a processor adapted to perform the method steps of any of the above first aspects when executing a program stored in the memory.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, having stored therein a computer program which, when executed by a processor, performs the method steps of any of the above-mentioned first aspects.
According to the vehicle identification method, the vehicle identification device and the electronic equipment, the similarity degree between 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, the appearance of the vehicle can not be obviously changed, and therefore whether the vehicle to be identified is the target vehicle can be judged according to the similarity degree, and vehicle identification of the vehicle which cannot acquire the license plate number can be achieved.
Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a vehicle identification method according to an embodiment of the present application;
FIG. 2 is a flow chart of a vehicle identification method provided by an embodiment of the present application;
FIG. 3 is a flow chart of a vehicle monitoring method according to an embodiment of the present disclosure;
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 provided in the 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 technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a vehicle identification method, which can be applied to an electronic device with a vehicle identification function, and in one possible application scenario, the electronic device can be a server for monitoring, and the server can be an electronic device with a function of performing data processing based on real-time flow calculation, for example, 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, streaming, and storm. In the embodiment of the present application, the server may perform vehicle monitoring based on SparkStreaming, Kafka (distributed publish-subscribe messaging system), and Redis (ruidis database).
In the vehicle monitoring process, the server can acquire a monitoring video containing all vehicles running on a road through video acquisition equipment arranged on the road, then, the server can regard 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 does not need to 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 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 may 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:
The vehicle to be identified is a vehicle that needs to be determined whether to be a target vehicle, and taking vehicle monitoring as an example, the vehicle to be identified may 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 vehicle-passing image or a historical vehicle-passing video.
The vehicle information includes vehicle model features obtained after vehicle modeling is performed on a shot picture or a shot video of a vehicle to be recognized, the vehicle model features are used for representing the appearance of the vehicle, the appearance of the vehicle can refer to the visual effect when the vehicle is observed from the outside of the vehicle, and it can be understood that the vehicle model features can also represent part of interior decoration in the vehicle due to the existence of a light-transmitting window body of the vehicle.
Taking the server applied to vehicle monitoring as an example, the server may be preset with a modeling analysis algorithm, such as OpenCV (Open source Computer Vision Library) and OpenGL (Open Graphics Library). In the related art, any algorithm having a function of performing three-dimensional reconstruction based on a video to obtain vehicle model features of a target object in the video may be used as a modeling analysis algorithm, and the embodiment of the present application is not particularly limited.
In an example, the server may perform three-dimensional reconstruction on each vehicle to be identified in the surveillance video through a modeling analysis algorithm and the surveillance video to obtain a vehicle model feature of the vehicle to be identified as vehicle information of the vehicle to be identified.
In a feasible implementation manner, the server may generate a monitoring number of each vehicle to be identified included in the monitoring video, and then, the server may correspondingly store the monitoring number and the vehicle model feature of the vehicle to be identified, so as to obtain the vehicle information of the vehicle to be identified.
And 102, if the vehicle model characteristics of the vehicle to be recognized are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle, determining that the vehicle to be recognized is the target vehicle.
In one possible implementation, the vehicle model features of the target vehicle stored in advance may be acquired, then, the similarity between the vehicle model features of the vehicle to be recognized and the vehicle model features of the target vehicle may be calculated, and then, the similarity may be compared with a preset similarity threshold. If the similarity is larger than the preset similarity threshold, the vehicle model feature of the vehicle to be recognized and the vehicle model feature of the target vehicle can be considered to be matched, and the vehicle to be recognized 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 as not matching, and further, the subsequent processing may not 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, assuming that the obtained similarity is 90%, and then the similarity is compared with a preset similarity threshold value of 80%, since the similarity 90% is greater than the preset similarity threshold value of 80%, it may be determined that the vehicle model feature matches the vehicle model feature of the target vehicle, and thus the vehicle to be identified is determined to be the target vehicle.
In the embodiment of the application, when the number of the target vehicles is multiple, it may be determined whether the vehicle model features of the vehicle to be recognized match the vehicle model features of the target vehicle for each target vehicle. For different target vehicles, the matching mode may be the same or different, and this embodiment is not limited to this.
It is understood that the vehicle model features may represent the appearance of the vehicle, and thus if the vehicle model features of the vehicle to be recognized and the vehicle model features of the target vehicle match, the appearance of the vehicle to be recognized may be considered to be similar to the appearance of the target vehicle to a higher degree. The appearance of one vehicle is not changed obviously, and the appearances of different vehicles are different, so that if the similarity between the appearance of the vehicle to be identified and the appearance of the target vehicle is higher, the vehicle to be identified can be regarded as the target vehicle.
When the target vehicle is a plurality of vehicles, the vehicle to be recognized may be determined to be a vehicle matching the vehicle model features of the vehicle to be recognized among the plurality of target vehicles. For example, assuming that a total of 3 target vehicles, respectively denoted as target vehicles 1 to 3, are included, 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 and 3, the vehicle to be identified is determined to be the target vehicle 2.
In the embodiment of the application, the vehicle model characteristics of the vehicle to be recognized and the vehicle model characteristics of the target vehicle can be matched, so that the similarity between the appearance of the vehicle to be recognized and the appearance of the target vehicle can be determined, the appearance of the vehicle can not be obviously changed, whether the vehicle to be recognized is the target vehicle can be judged according to the similarity, and the vehicle recognition of the vehicle which cannot acquire the license plate number can be realized.
Optionally, the vehicle information may further include other vehicle characteristics in addition to the vehicle model characteristics, and the other vehicle characteristics may include at least one of a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color. Among these, vehicle brands such as BMW, Mazda; sub-brands of vehicles such as a morning BMW, a Mount of Automation; vehicle models such as cars, off-road vehicles; the vehicle color is, for example, black or silver. The other vehicle features may be vehicle features obtained after modeling a shot picture or a shot video of the vehicle to be identified.
After the above-mentioned 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 features of the vehicle to be recognized and the vehicle model features of the target vehicle match, the vehicle information of the vehicle to be recognized and the vehicle information of the target vehicle may be compared, and whether to further determine whether the vehicle model features of the vehicle to be recognized and the vehicle model features of the target vehicle match or not may be determined according to the comparison result.
The specific treatment process comprises the following steps:
step 1, vehicle information of a vehicle to be identified is obtained.
In the implementation, the specific processing procedure of this step is the same as that of step 101, and is not described herein again.
And 2, 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.
In one possible embodiment, if it is determined that the comparison result is different from the preset comparison result, the subsequent process may not be performed.
In the embodiment of the application, for different types of the acquired vehicle information, the manner of comparing the other vehicle characteristics of the vehicle to be identified with the other vehicle characteristics of the target vehicle is also different. When there are at least two other vehicle characteristics, it may be that each other vehicle characteristic of the vehicle to be identified and the same kind of other vehicle characteristic of the target vehicle are compared in turn. 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 obtained, 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, the priority can be compared according to the preset other vehicle characteristics, and each other vehicle characteristic of the vehicle to be identified and the same other vehicle characteristics of the target vehicle can be sequentially compared to obtain a comparison result. The other vehicle characteristic comparison priorities may be, in order from 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 needs, and the embodiment of the present application does not limit the specific form of the comparison priority of the vehicle information.
And aiming at the difference of the types of the other acquired vehicle characteristics of the vehicle to be identified, the preset comparison results are also different. 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 is the same as the other vehicle characteristic of the target vehicle. When the other vehicle characteristics include at least two vehicle characteristics, the preset comparison result may be set according to actual requirements.
For example, in one possible embodiment, the preset comparison result may be that each vehicle characteristic included in the other vehicle characteristics is the same. In another possible embodiment, the preset comparison result may be that the number of the same other vehicle characteristics is greater than or equal to (or greater than) a preset category threshold, and the preset category threshold may be set according to actual requirements, for example, if the other vehicle characteristics include 5 and the preset category threshold is 3, the comparison result may be determined as the preset comparison result when the vehicle to be identified and the target vehicle have three or more same other vehicle characteristics. In another possible embodiment, a 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 features is greater than or equal to (or greater than in other possible embodiments) a preset numerical threshold, and the preset numerical threshold may be set according to actual requirements, for example, if the preset numerical threshold is 3, the vehicle to be identified and the target vehicle have the same vehicle brand, vehicle sub-brand, and vehicle color, and if 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, since the sum of the preset weights of the same other vehicle features is equal to 3, the comparison result may be determined to be the preset comparison result. In other possible embodiments, the preset comparison result may be in other forms, and this embodiment does not limit this.
Step 3, if the vehicle model characteristics of the vehicle to be recognized are determined to be matched with the vehicle model characteristics of the pre-stored target vehicle, determining that the vehicle to be recognized is the target vehicle
This step is the same as the step 102, and reference may be made to the related description in the foregoing S102, which is not described herein again.
As described in connection with step 2 above, it is understood that, depending on the setting of the preset comparison result, in some possible embodiments, it may not be necessary to perform a comparison between each other vehicle characteristic of the vehicle to be identified and the same other vehicle characteristic of the target vehicle, and it may be possible to determine whether the comparison result is the preset comparison result. For example, if the number of the types of the other vehicle features with the same preset standard comparison result is greater than or equal to 5, after the comparison of the three other vehicle features is completed, the three other vehicle features of the vehicle to be identified and the target vehicle are all the same, it may be determined that the comparison result is the preset comparison result without comparing whether the remaining two other vehicle features of the vehicle to be identified and the target vehicle are the same.
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, as shown in fig. 2, the following steps may be specifically included:
In an implementation, 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 vehicles are of different brands, step 206 may be performed.
In the embodiment of the application, the manner of judging whether the vehicle brand of the vehicle to be identified is the same as the vehicle brand of the target vehicle can be diversified. 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 the same, 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.
In another possible implementation, a similarity between the field representing the vehicle brand of the vehicle to be identified and the field representing the vehicle brand of the target vehicle may also be calculated, and then the similarity may be compared with 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 similarity of a field "mazda" representing the vehicle brand of the vehicle to be identified and a field "mazda" representing the vehicle brand of the target vehicle may be calculated to obtain 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.
In an implementation, 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, step 203 may be performed; if the two vehicle sub-brands are not the same, step 206 may be performed.
In an implementation, 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 vehicle models are the same, step 204 may be performed; if the vehicle styles are not the same, step 206 may be performed.
And step 204, judging whether the vehicle color of the vehicle to be identified is the same as the vehicle color of the target vehicle.
In an implementation, 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 the same color, step 206 may be performed.
In the implementation, the specific processing procedure of this step is similar to that of step 102, and is not described here again.
In the embodiment of the application, the obtained other vehicle characteristics 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 correspondingly, when different types of other vehicle characteristics of the vehicle to be identified are obtained, the priorities are compared according to the other vehicle characteristic information, and specific processing procedures for comparing each type of other vehicle characteristics of the vehicle to be identified with the other vehicle characteristics of the target vehicle are also different. For different types of other vehicle features, the process of comparing the other vehicle features of the vehicle to be recognized with the other vehicle features of the target vehicle is similar to steps 201 to 206, and is not repeated here.
In the embodiment of the application, vehicle information such as the vehicle brand, the vehicle sub-brand, the vehicle style and the vehicle color of the vehicle to be identified can be acquired. Before matching the vehicle model features of the vehicle to be recognized with the vehicle model features of the target vehicle, the other vehicle features of the vehicle to be recognized may be sequentially compared with the other vehicle features of the target vehicle of the corresponding category. Therefore, based on 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 can be determined based on various dimensions, and the comparison accuracy 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 is judged based on the degree of correlation of other observable vehicle characteristics, the vehicle to be identified with low degree of correlation with the target vehicle can be removed to the maximum extent, and the calculation resources required by vehicle identification are reduced.
Alternatively, a vehicle information filtering condition for filtering vehicle information that does not include the vehicle model feature may be set in advance. After the vehicle information of the vehicle to be identified is obtained, whether the vehicle information meets the vehicle information filtering condition or not is judged through the vehicle information filtering condition and the vehicle information, namely whether modeling analysis aiming at the vehicle to be identified is successful or not is judged, and whether the vehicle model characteristic of the vehicle to be identified is 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 step of determining the similarity between the vehicle model characteristic of the vehicle to be identified and the vehicle model characteristic of the target vehicle stored in advance.
In implementation, if the vehicle information of the vehicle to be recognized does not satisfy the preset vehicle information filtering condition, it may be determined that the vehicle model feature of the vehicle to be recognized is successfully acquired, and then step 102 may be performed to determine whether the vehicle to be recognized 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 can be omitted.
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 characteristic of the vehicle to be identified is judged, and the similarity between the vehicle model characteristic of the vehicle to be identified and the vehicle model characteristic of the target vehicle can be conveniently calculated subsequently. Therefore, the comparison between the vehicle to be identified and the target vehicle based on the invalid vehicle information can be avoided, the calculation resources required by 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 a stream processing framework, so that a server for vehicle monitoring can store vehicle information of a target vehicle in a local database, and the server can also store the vehicle information of the target vehicle in a non-relational database. Examples of the non-relational database include a Redis database and an Hbase database.
When receiving an update instruction of the vehicle information of the target vehicle, the server may add or delete the locally stored vehicle information of the target vehicle, but may not change the vehicle information of the target vehicle in the non-relational database, and therefore, 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 before acquiring the vehicle information of the target vehicle. As shown in fig. 3, the method specifically includes the following steps:
In an implementation, if the vehicle information of the target vehicle is stored in the non-relational database, the server may obtain an update record of the database, and the update record may record an update time of the vehicle information in the database.
The server may compare the most recent update time of the vehicle information in the update record to the update time of the stored vehicle information in the non-relational database. If the most recent update time is the same as the update time, the server may determine that the vehicle information of the target vehicle in the non-relational database does not need to be updated; if the most recent 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.
And 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 the embodiment of the application, when the vehicle information of the target vehicle is stored based on the Redis database, the server may create a monitoring process for the database through spark streaming, and the server may obtain the update identifier of the database through the monitoring process before obtaining the vehicle information of the target vehicle. If the update flag indicates that the database is updated again after the non-relational database was last updated, the server may determine that the vehicle information of the target vehicle in the non-relational database needs to be updated. The server may then obtain the vehicle information in the database and store the obtained vehicle information in the Redis database.
In the embodiment of the application, if the vehicle information of the target vehicle is stored in the non-relational database in a cache manner, 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 the accuracy of vehicle monitoring are improved.
Optionally, after determining that the vehicle to be identified is the target vehicle, vehicle information of the vehicle to be identified may be recorded, and the recording process 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, the vehicle number of the vehicle to be identified may be acquired as the vehicle identifier of the vehicle to be identified, and then, the vehicle identifier of the vehicle to be identified and the vehicle information of the vehicle to be identified may be correspondingly stored.
In one possible implementation mode, a preset special identifier may be added to the vehicle information of the vehicle to be identified 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 matches the vehicle information of a plurality of target vehicles, the vehicle number 001 of the vehicle to be identified may be stored in a preset vehicle blacklist, and new vehicle numbers 001 and 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 identification of the vehicle to be identified can be acquired, and the vehicle identification of the vehicle to be identified and the vehicle information of the vehicle to be identified are correspondingly stored. Therefore, the monitoring video containing the vehicle to be identified is conveniently acquired based on the vehicle information of the vehicle to be identified, and the target vehicle is subjected to monitoring processing such as positioning, tracking and running track checking.
The embodiment of the application further provides an implementation manner that the server monitors the vehicle based on spark timing, and the specific processing process includes: 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 may create a monitoring process for the database through spark monitoring, so as to obtain an update identifier of the database through the monitoring process, and subsequently determine whether to update the vehicle information of the target vehicle in the Redis database according to the update identifier.
Meanwhile, the server may acquire the vehicle information of the vehicle to be identified from kafka through Sparkstreaming. Because the vehicle information of the vehicle to be identified acquired by the server through the kafka may include vehicle information that is not subjected to modeling analysis, or the vehicle information of the vehicle model feature is not acquired because the modeling analysis of the vehicle appearance feature fails, the server may convert the vehicle information of the vehicle to be identified into a map object, delete the vehicle information after the non-modeling analysis and the vehicle information after the modeling analysis by a preset filter operator, a vehicle information filtering condition and the vehicle information.
Then, the server can acquire the updated vehicle information of the target vehicle in the Redis database through spark timing, 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 characteristic. If the comparison result shows 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 the vehicle information set of the target vehicle. The server may send the set of vehicle information to topoc of kafka.
An embodiment of the present application further provides a vehicle identification apparatus, as shown in fig. 4a, the apparatus includes:
the acquiring module 410 is configured to acquire vehicle information of a vehicle to be identified, where the vehicle information includes vehicle model features obtained after modeling a captured picture or a captured video of the vehicle to be identified;
a matching module 420, 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 a vehicle model feature of a 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 after modeling a shot picture or a shot video of the vehicle to be identified; the device further comprises:
and the comparison module 430 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.
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 kind of other vehicle features of the pre-stored target vehicle in an order from a high priority to a low priority of each other vehicle feature;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
In one possible embodiment, the other vehicle characteristics include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; 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 is determined.
An embodiment of the present application further 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 the vehicle identification method when executing the program stored in the memory 501:
the method comprises the steps of obtaining vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified;
and if the vehicle model features of the vehicle to be recognized are determined to be matched with the vehicle model features of the pre-stored target vehicle, determining that the vehicle to be recognized 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 shot picture or a shot video of the vehicle to be identified; before the determining that the vehicle model features of the vehicle to be identified match the vehicle model features of the pre-stored target vehicle, 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 between 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 includes:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristics of the pre-stored target vehicle according to the sequence of the preset priority of each other vehicle characteristic 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 characteristics include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; 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 is determined.
The Memory mentioned in the above electronic device may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
In yet another embodiment provided by the present application, there is also provided a computer readable storage medium having a computer program stored therein, the computer program, when executed by a processor, implementing 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, the implementation may be wholly or partially realized 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, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (10)
1. A vehicle identification method, characterized in that the method comprises:
the method comprises the steps of obtaining vehicle information of a vehicle to be identified, wherein the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified;
and if the vehicle model features of the vehicle to be recognized are determined to be matched with the vehicle model features of the pre-stored target vehicle, determining that the vehicle to be recognized is the target vehicle.
2. The method according to claim 1, characterized in that the vehicle information further comprises other vehicle features besides the vehicle model features, wherein the other vehicle features are vehicle features obtained after modeling a shot picture or a shot video of the vehicle to be identified; before the determining that the vehicle model features of the vehicle to be identified match the vehicle model features of the pre-stored target vehicle, 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.
3. The method of claim 2, wherein the other vehicle features include at least two;
the determining that the comparison result between 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 includes:
sequentially comparing each other vehicle characteristic of the vehicle to be identified with the same other vehicle characteristics of the pre-stored target vehicle according to the sequence of the preset priority of each other vehicle characteristic from high to low;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
4. The method of claim 2, wherein the other vehicle characteristics include a vehicle brand, a vehicle sub-brand, a vehicle style, and a vehicle color; 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 is determined.
5. A vehicle identification device, characterized in that the device comprises:
the vehicle information comprises vehicle model characteristics obtained after modeling of a shot picture or a shot video of the vehicle to be identified;
and the matching module is used for determining that the vehicle to be identified is 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.
6. The apparatus according to claim 5, wherein the vehicle information further includes other vehicle features in addition to the vehicle model feature, the other vehicle features being vehicle features obtained after modeling a taken picture or a taken video of the vehicle to be identified; the device further comprises:
and 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.
7. The apparatus of claim 6, wherein 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 kind of other vehicle features of the pre-stored target vehicle in an order from a high priority to a low priority of each other vehicle feature;
and determining the comparison result of all the other vehicle characteristics as a preset comparison result.
8. The apparatus of claim 6, 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 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 is determined.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 4 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
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