CN117496455A - Vehicle behavior recognition method, device, computer equipment and storage medium - Google Patents
Vehicle behavior recognition method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle behavior recognition method, a vehicle behavior recognition device, computer equipment and a storage medium. The method comprises the following steps: acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image; detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image; if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image. By the method, the vehicle identification accuracy can be improved.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a vehicle behavior recognition method, apparatus, computer device, and storage medium.
Background
In expressway toll operation, a trailer is thrown and changed to be a common fee escaping means, and the specific operation is that after the trailer enters a service area, a carriage is placed in the service area, and then a driver drives the vehicle head to get on a card after the vehicle head is at a high speed under a nearby toll station, and the vehicle head is pulled up to the service area under the adjacent toll station, so that the traffic fee can be reduced; or the two vehicles exchange carriages, and the charging is reduced by reducing the weight of the cargoes.
At present, the swing and change behavior is only carried out on a toll station at a high-speed exit, and manual identification is carried out by personnel of the toll station in a mode of checking monitoring records, but the method has low efficiency and low identification success rate, so that a trailer swing and change identification method capable of increasing identification efficiency and success rate is urgently needed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a vehicle behavior recognition method, apparatus, computer device, and storage medium that can increase the accuracy of vehicle recognition.
In a first aspect, the present application provides a vehicle behavior recognition method. The method comprises the following steps:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In one embodiment, the vehicle feature image includes a vehicle matching feature image and an exit swing-hanging feature image, and determining whether a target vehicle has a swing-hanging behavior according to the vehicle feature image includes:
Searching and obtaining an entrance throwing and hanging feature image of a target vehicle at a road entrance according to the vehicle matching feature image;
and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
In one embodiment, the vehicle matching feature image is a vehicle head image, and according to the vehicle matching feature image, searching for an entrance throwing and hanging feature image of a target vehicle at a road entrance includes:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image;
and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
In one embodiment, determining whether the target vehicle has a swap behavior according to a degree of similarity between the exit swap feature image and the entrance swap feature image includes:
inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into a feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image;
And determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
In one embodiment, the exit spin feature vector includes an exit body feature vector, the entry spin feature vector includes an entry body feature vector, and determining whether a target vehicle has a spin behavior according to a degree of similarity between the exit spin feature vector and the entry spin feature vector includes:
obtaining a second similarity between the exit body feature vector and the entrance body feature vector;
and if the second similarity is smaller than the second threshold value, determining that the target vehicle has a throwing and hanging action.
In one embodiment, the exit spin feature vector further comprises an exit fender feature vector, the entry spin feature vector further comprises an entry fender feature vector, and the method further comprises:
if the second similarity is greater than or equal to a second threshold, acquiring a third similarity between the feature vector of the outlet fender and the feature vector of the inlet fender;
if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior;
and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
In one embodiment, acquiring a vehicle type image and a vehicle feature image of a target vehicle from a vehicle side image includes:
inputting the vehicle side image into a target monitoring model to obtain vehicle position information;
and according to the position information of the vehicle part, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
In one embodiment, the vehicle type image includes a wheel image, and detecting whether the vehicle type of the target vehicle is a trailer type based on the vehicle type image includes:
inputting the wheel images into a wheel detection model, and determining the number of wheels of a target vehicle;
if the number of wheels is greater than the threshold number of wheels, the vehicle type of the target vehicle is determined to be a trailer type.
In a second aspect, the present application also provides a vehicle behavior recognition device. The device comprises:
the image acquisition module is used for acquiring a vehicle side image of a target vehicle at a road exit and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
the type detection module is used for detecting whether the vehicle type of the target vehicle is the trailer type according to the vehicle type image;
And the behavior recognition module is used for determining whether the target vehicle has a throwing-changing behavior according to the vehicle characteristic image if the vehicle type is the trailer type.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In a fourth aspect, the present application also provides a computer-readable storage medium. A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
If the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In a fifth aspect, the present application also provides a computer program product. Computer program product comprising a computer program which, when executed by a processor, realizes the steps of:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
The vehicle behavior recognition method, the vehicle behavior recognition device, the computer equipment and the storage medium. Firstly, acquiring a vehicle side image at a road exit, and then acquiring a vehicle type image and a vehicle characteristic image of a target vehicle according to the vehicle side image, so as to judge whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image, and judging whether the target vehicle has a throwing and hanging action according to the vehicle characteristic image only when the target vehicle is judged to be the trailer type. According to the method, only the vehicle side image of the target vehicle at the road exit is required to be acquired, and whether the target vehicle has the throwing-changing hanging behavior or not can be judged according to the vehicle side image.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a diagram of an application environment for a vehicle behavior recognition method in one embodiment;
FIG. 2 is a flow chart of a method of vehicle behavior recognition in one embodiment;
FIG. 3 is a flow chart of determining whether a target has a swap hanging behavior in one embodiment;
FIG. 4 is a flow chart of a method for identifying vehicle behavior in another embodiment;
FIG. 5 is a block diagram showing the structure of a vehicle behavior recognition apparatus in one embodiment;
FIG. 6 is a block diagram showing the structure of a vehicle behavior recognition apparatus in another embodiment;
fig. 7 is a block diagram showing the structure of a vehicle behavior recognition apparatus in yet another embodiment;
FIG. 8 is a block diagram showing the structure of a vehicle behavior recognition apparatus in still another embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The vehicle behavior recognition method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in FIG. 1. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data required for the relevant processing. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement the vehicle behavior recognition method shown in any of the embodiments described below.
In one embodiment, as shown in fig. 2, a vehicle behavior recognition method is provided, and the method is applied to the computer device in fig. 1 for illustration, and includes the following steps:
s201, acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image.
The road exit is a toll station of the expressway exit; the vehicle side image is an image of the side of the target vehicle; the vehicle type image is an image for characterizing a target vehicle type; the vehicle feature image is an image for characterizing a feature of the target vehicle.
Specifically, when the target vehicle passes through the road exit, the side face of the target vehicle is shot by using a camera at the road exit, so that a vehicle side face image of the target vehicle can be obtained, the vehicle side panel image is cut into two parts on average, the image containing the head part is used as a vehicle type image of the target vehicle, and the image containing the tail part is used as a vehicle characteristic image of the target vehicle.
Optionally, the method for acquiring the vehicle type image and the vehicle feature image of the target vehicle may further include inputting the vehicle side image into the target monitoring model to obtain the vehicle position information; and according to the position information of the vehicle part, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
Specifically, the vehicle side image is input into the target monitoring model, and the target monitoring model outputs vehicle part position information, and at this time, the vehicle side image may be divided into a vehicle type image and a vehicle feature image according to the vehicle part position information.
S202, detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image.
Specifically, a relation mapping table between the vehicle type image and the vehicle type is manufactured in advance, and after the vehicle type image is determined, whether the vehicle type of the target vehicle is a trailer or not can be determined according to a table look-up mode.
Optionally, the vehicle type image includes a wheel image, and the method for determining the vehicle type of the target vehicle may further include inputting the wheel image into a wheel detection model to determine the number of wheels of the target vehicle; if the number of wheels is greater than the threshold number of wheels, the vehicle type of the target vehicle is determined to be a trailer type.
For example, the wheel image of the target vehicle is input into the wheel detection model, the wheel detection model outputs the number of wheels of the target vehicle, when the number of wheels is greater than 4, the type of the target vehicle is determined to be the trailer type, and when the number of wheels is less than or equal to 4, the type of the target vehicle is determined to be not the trailer type, and at this time, whether the target vehicle has the dump hanging action or not does not need to be continuously judged.
And S203, if the vehicle type is the trailer type, determining whether the target vehicle has a throwing and hanging action according to the vehicle characteristic image.
Specifically, when the vehicle type of the target vehicle is determined to be the trailer type, acquiring a vehicle characteristic image of the target vehicle at a road entrance from a storage system of the computer equipment, judging whether the vehicle characteristic image of the target vehicle at the road entrance is consistent with the vehicle characteristic image of the target vehicle at a road exit, if so, determining that the target vehicle does not have a throwing and changing behavior; if not, determining that the target vehicle has a throwing and hanging action.
In the above embodiment, the vehicle side image at the road exit is acquired first, and then the vehicle type image and the vehicle feature image of the target vehicle are acquired according to the vehicle side image, so as to determine whether the vehicle type of the target vehicle is the trailer type according to the vehicle type image, and only when the target vehicle is determined to be the trailer type, determine whether the target vehicle has the swing-change behavior according to the vehicle feature image. According to the method, only the vehicle side image of the target vehicle at the road exit is required to be acquired, and whether the target vehicle has the throwing-changing hanging behavior or not can be judged according to the vehicle side image.
The above embodiment describes the vehicle behavior recognition method as a whole, and the most important is to determine whether the target vehicle has a swing and hang behavior, in this embodiment, the vehicle feature image includes a vehicle matching feature image and an exit swing and hang feature image, as shown in fig. 3, and the detailed description of the specific method includes:
s301, searching and obtaining an entrance throwing and hanging feature image of the target vehicle at the entrance of the road according to the vehicle matching feature image.
Specifically, the vehicle matching feature image may be stored in association with a license plate of the target vehicle in a storage system of the computer device, and the license plate of the target vehicle may be used as a keyword, and an entry swing feature image of the target vehicle at the road entrance may be searched in the storage system of the computer device.
Optionally, the vehicle matching feature image may be a headstock image, and the headstock image may be input into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image; and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
Specifically, inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image, acquiring all inlet headstock feature vectors from a storage system of computer equipment, calculating first similarity between the outlet headstock feature vector of the target vehicle and each inlet headstock feature vector, and acquiring an inlet swing and change feature image corresponding to the inlet headstock feature vector with the highest first similarity.
S302, determining whether the target vehicle has a throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
Specifically, the outlet throwing-changing feature image and the inlet throwing-changing feature image are input into a similarity confirmation model to obtain similarity between the outlet throwing-changing feature image and the inlet throwing-changing feature image, and whether the target vehicle has throwing-changing behaviors or not is determined according to the relationship between the similarity and a similarity threshold; for example, if the similarity is greater than the similarity threshold, determining that the target vehicle does not have a swing-to-hang behavior; and if the similarity is smaller than or equal to the similarity threshold value, determining that the target vehicle has a throwing and hanging action.
Optionally, the method for determining whether the target vehicle has the throwing-changing behavior may further include inputting the outlet throwing-changing feature image and the inlet throwing-changing feature image into the feature extraction model to obtain an outlet throwing-changing feature vector corresponding to the outlet throwing-changing feature image and an inlet throwing-changing feature vector corresponding to the inlet throwing-changing feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
Specifically, the outlet throwing and hanging feature image and the inlet throwing and hanging feature image are input into a feature extraction model, the feature extraction model outputs an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image, whether the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector is larger than a preset similarity degree threshold value or not is judged, and if yes, the fact that the throwing and hanging behavior of the target vehicle does not exist is determined; if not, determining that the target vehicle has a throwing and hanging action.
Optionally, the exit spin feature vector includes an exit body feature vector, and the entrance spin feature vector includes an entrance body feature vector, so that a second similarity between the exit body feature vector and the entrance body feature vector may also be obtained; and if the second similarity is smaller than the second threshold value, determining that the target vehicle has a throwing and hanging action.
The method includes the steps that a second similarity between an outlet vehicle body feature vector and an inlet vehicle body feature vector is obtained, whether the second similarity is larger than or equal to a second threshold value is judged, and if yes, it is determined that a target vehicle does not have a throwing-changing hanging action; if not, determining that the target vehicle has a throwing and hanging action.
Optionally, the outlet swing-shift feature vector further includes an outlet fender feature vector, and if the second similarity is greater than or equal to a second threshold, a third similarity between the outlet fender feature vector and the inlet fender feature vector is obtained; if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior; and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
Specifically, in order to increase the accuracy of vehicle behavior recognition, when the second similarity is determined to be greater than or equal to a second threshold value, a third similarity between the feature vector of the exit fender and the feature vector of the entrance fender is also required to be obtained, whether the third similarity is greater than or equal to the third threshold value is judged, and if so, it is determined that the target vehicle does not have a throwing-changing behavior; if not, determining that the target vehicle has a throwing and hanging action.
In the above embodiment, according to the matching feature image of the vehicle, the entrance swing feature image of the target vehicle at the entrance of the road is searched for; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image. Compared with a manual checking video method, the accuracy of vehicle behavior recognition is greatly improved.
In order to more fully demonstrate the present solution, this embodiment provides an alternative way of identifying the behavior of a vehicle, as shown in fig. 4:
s401, acquiring a vehicle side image of a target vehicle at a road exit.
S402, inputting the vehicle side image into the target monitoring model to obtain vehicle part position information.
S403, according to the vehicle position information, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
S404, inputting the wheel image into the wheel detection model, and determining the number of wheels of the target vehicle.
And S405, if the number of wheels is greater than the threshold value of the number of wheels, determining that the vehicle type of the target vehicle is the trailer type.
S406, if the vehicle type is a trailer type, inputting the headstock image into the feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image.
S407, obtaining first similarity between the feature vector of the exit headstock and the feature vector of the entrance headstock of each vehicle at the entrance of the road, and taking the swing and change feature image corresponding to the vehicle with the first similarity larger than a first threshold value as an entrance swing and change feature image.
S408, inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into the feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image.
S409, obtaining a second similarity between the outlet bodywork feature vector and the inlet bodywork feature vector.
And S410, if the second similarity is smaller than a second threshold value, determining that the target vehicle has a throwing and hanging action.
S411, if the second similarity is greater than or equal to the second threshold, a third similarity between the exit fender feature vector and the entrance fender feature vector is obtained.
And S412, if the third similarity is smaller than the third threshold value, determining that the target vehicle has a throwing and hanging action.
And S413, if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing-changing hanging action.
The specific process of S401 to S413 may be referred to the description of the above method embodiment, and its implementation principle and technical effect are similar, and will not be described herein.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a vehicle behavior recognition device for realizing the vehicle behavior recognition method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the vehicle behavior recognition device or devices provided below may refer to the limitation of the vehicle behavior recognition method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 5, there is provided a vehicle behavior recognition apparatus 5 including: an image acquisition module 50, a type detection module 51 and a behavior recognition module 52, wherein:
an image acquisition module 50 for acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle feature image of the target vehicle from the vehicle side image;
a type detection module 51 for detecting whether the vehicle type of the target vehicle is a trailer type based on the vehicle type image;
the behavior recognition module 52 is configured to determine whether the target vehicle has a swing-over behavior according to the vehicle feature image if the vehicle type is a trailer type.
In another embodiment, as shown in fig. 6, the behavior recognition module 52 in fig. 5 includes:
The image searching unit 520 is configured to search for an entry swing feature image of the target vehicle at the road entrance according to the vehicle matching feature image;
the behavior recognition unit 521 is configured to determine whether the target vehicle has a change behavior according to a degree of similarity between the exit change feature image and the entrance change feature image.
In another embodiment, the image searching unit 520 in fig. 6 is specifically configured to:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image; and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
In another embodiment, the behavior recognition unit 521 in fig. 6 includes:
the vector acquisition subunit is used for inputting the outlet throwing-changing feature image and the inlet throwing-changing feature image into the feature extraction model to obtain an outlet throwing-changing feature vector corresponding to the outlet throwing-changing feature image and an inlet throwing-changing feature vector corresponding to the inlet throwing-changing feature image;
And the behavior recognition subunit is used for determining whether the target vehicle has the throwing and hanging behavior according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
In another embodiment, the behavior recognition subunit in the foregoing embodiment is specifically configured to:
obtaining a second similarity between the exit body feature vector and the entrance body feature vector; if the second similarity is smaller than a second threshold value, determining that the target vehicle has a throwing-changing hanging behavior; if the second similarity is greater than or equal to a second threshold, acquiring a third similarity between the feature vector of the outlet fender and the feature vector of the inlet fender; if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior; and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
In another embodiment, as shown in fig. 7, the image acquisition module 50 in fig. 5 includes:
a position determining unit 500 for inputting the vehicle side image into the target monitoring model to obtain vehicle position information;
the image acquisition unit 501 is configured to perform segmentation processing on a vehicle side image according to vehicle part position information, so as to obtain a vehicle type image and a vehicle feature image.
In another embodiment, as shown in fig. 8, the type detection module 51 in fig. 5 includes:
a number determination unit 510 for inputting the wheel images into the wheel detection model, determining the number of wheels of the target vehicle;
the type detection unit 511 is configured to determine that the vehicle type of the target vehicle is the trailer type if the number of wheels is greater than the threshold number of wheels.
The respective modules in the above-described vehicle behavior recognition apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing vehicle side image data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle behavior recognition method.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a vehicle behavior recognition method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In one embodiment, the processor when executing the computer program further performs the steps of:
searching and obtaining an entrance throwing and hanging feature image of a target vehicle at a road entrance according to the vehicle matching feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image; and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into a feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
In one embodiment, the processor when executing the computer program further performs the steps of:
obtaining a second similarity between the exit body feature vector and the entrance body feature vector; and if the second similarity is smaller than the second threshold value, determining that the target vehicle has a throwing and hanging action.
In one embodiment, the processor when executing the computer program further performs the steps of:
if the second similarity is greater than or equal to a second threshold, acquiring a third similarity between the feature vector of the outlet fender and the feature vector of the inlet fender; if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior; and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the vehicle side image into a target monitoring model to obtain vehicle position information; and according to the position information of the vehicle part, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the wheel images into a wheel detection model, and determining the number of wheels of a target vehicle; if the number of wheels is greater than the threshold number of wheels, the vehicle type of the target vehicle is determined to be a trailer type.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
searching and obtaining an entrance throwing and hanging feature image of a target vehicle at a road entrance according to the vehicle matching feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image; and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into a feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a second similarity between the exit body feature vector and the entrance body feature vector; and if the second similarity is smaller than the second threshold value, determining that the target vehicle has a throwing and hanging action.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the second similarity is greater than or equal to a second threshold, acquiring a third similarity between the feature vector of the outlet fender and the feature vector of the inlet fender; if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior; and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the vehicle side image into a target monitoring model to obtain vehicle position information; and according to the position information of the vehicle part, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the wheel images into a wheel detection model, and determining the number of wheels of a target vehicle; if the number of wheels is greater than the threshold number of wheels, the vehicle type of the target vehicle is determined to be a trailer type.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
if the vehicle type is the trailer type, determining whether the target vehicle has a swing and hanging action according to the vehicle characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
searching and obtaining an entrance throwing and hanging feature image of a target vehicle at a road entrance according to the vehicle matching feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image; and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road inlet, and taking the throwing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as an inlet throwing and hanging characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into a feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image; and determining whether the target vehicle has the throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a second similarity between the exit body feature vector and the entrance body feature vector; and if the second similarity is smaller than the second threshold value, determining that the target vehicle has a throwing and hanging action.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the second similarity is greater than or equal to a second threshold, acquiring a third similarity between the feature vector of the outlet fender and the feature vector of the inlet fender; if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior; and if the third similarity is greater than or equal to a third threshold value, determining that the target vehicle does not have the throwing and hanging action.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the vehicle side image into a target monitoring model to obtain vehicle position information; and according to the position information of the vehicle part, dividing the vehicle side image to obtain a vehicle type image and a vehicle characteristic image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Inputting the wheel images into a wheel detection model, and determining the number of wheels of a target vehicle; if the number of wheels is greater than the threshold number of wheels, the vehicle type of the target vehicle is determined to be a trailer type.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not thereby to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (12)
1. A vehicle behavior recognition method, characterized in that the method comprises:
acquiring a vehicle side image of a target vehicle at a road exit, and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
And if the vehicle type is the trailer type, determining whether the target vehicle has a swing and change behavior according to the vehicle characteristic image.
2. The method of claim 1, wherein the vehicle feature image comprises a vehicle matching feature image and an exit swap-hang feature image, the determining whether the target vehicle has a swap-hang behavior based on the vehicle feature image comprising:
searching and obtaining an entrance throwing and hanging feature image of the target vehicle at a road entrance according to the vehicle matching feature image;
and determining whether the target vehicle has a throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature image and the inlet throwing and hanging feature image.
3. The method according to claim 2, wherein the vehicle matching feature image is a vehicle head image, and the searching for an entrance swing feature image of the target vehicle at the road entrance according to the vehicle matching feature image includes:
inputting the headstock image into a feature extraction model to obtain an outlet headstock feature vector corresponding to the headstock image;
and acquiring a first similarity between the outlet headstock characteristic vector and the inlet headstock characteristic vector of each vehicle at the road entrance, and taking a swing and hanging characteristic image corresponding to the vehicle with the first similarity larger than a first threshold value as the inlet swing and hanging characteristic image.
4. The method of claim 2, wherein the determining whether the target vehicle has a swap behavior based on a degree of similarity between the exit swap feature image and the entrance swap feature image comprises:
inputting the outlet throwing and hanging feature image and the inlet throwing and hanging feature image into a feature extraction model to obtain an outlet throwing and hanging feature vector corresponding to the outlet throwing and hanging feature image and an inlet throwing and hanging feature vector corresponding to the inlet throwing and hanging feature image;
and determining whether the target vehicle has a throwing and hanging action according to the similarity degree between the outlet throwing and hanging feature vector and the inlet throwing and hanging feature vector.
5. The method of claim 4, wherein the exit swap feature vector comprises an exit body feature vector, the entrance swap feature vector comprises an entrance body feature vector, and determining whether the target vehicle has a swap behavior based on a degree of similarity between the exit swap feature vector and the entrance swap feature vector comprises:
acquiring a second similarity between the exit body feature vector and the entrance body feature vector;
And if the second similarity is smaller than a second threshold value, determining that the target vehicle has a throwing and hanging action.
6. The method of claim 5, wherein the outlet kick feature vector further comprises an outlet fender feature vector, the inlet kick feature vector further comprises an inlet fender feature vector, the method further comprising:
if the second similarity is greater than or equal to the second threshold, acquiring a third similarity between the outlet fender feature vector and the inlet fender feature vector;
if the third similarity is smaller than a third threshold value, determining that the target vehicle has a throwing-changing hanging behavior;
and if the third similarity is greater than or equal to the third threshold, determining that the target vehicle does not have the throwing and hanging action.
7. The method of claim 1, wherein the acquiring a vehicle type image and a vehicle feature image of the target vehicle from the vehicle side image comprises:
inputting the vehicle side image into a target monitoring model to obtain vehicle position information;
and according to the vehicle position information, carrying out segmentation processing on the vehicle side image to obtain the vehicle type image and the vehicle characteristic image.
8. The method of claim 1, wherein the vehicle type image comprises a wheel image, and wherein the detecting whether the vehicle type of the target vehicle is a trailer type based on the vehicle type image comprises:
inputting the wheel images into a wheel detection model, and determining the number of wheels of the target vehicle;
and if the number of the wheels is larger than the threshold value of the number of the wheels, determining that the vehicle type of the target vehicle is a trailer type.
9. A vehicle behavior recognition apparatus, characterized in that the apparatus comprises:
the image acquisition module is used for acquiring a vehicle side image of a target vehicle at a road exit and acquiring a vehicle type image and a vehicle characteristic image of the target vehicle according to the vehicle side image;
the type detection module is used for detecting whether the vehicle type of the target vehicle is a trailer type according to the vehicle type image;
and the behavior recognition module is used for determining whether the target vehicle has a throwing and hanging behavior according to the vehicle characteristic image if the vehicle type is the trailer type.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 8.
12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 8.
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