CN110796698B - Vehicle weight removing method and device with maximum area and minimum length-width ratio - Google Patents
Vehicle weight removing method and device with maximum area and minimum length-width ratio Download PDFInfo
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Abstract
The invention discloses a vehicle weight-removing method and a vehicle weight-removing device with the largest area and the smallest length-width ratio.A vehicle image comprising a rectangular frame of a complete vehicle is obtained by obtaining frame images in a video and carrying out vehicle detection on each frame image through a Yolo algorithm; generating a vehicle ID in the vehicle image through a target tracking algorithm, and obtaining a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID; respectively calculating the area index and the aspect ratio index of a rectangular frame of each vehicle image in the vehicle image set, and comparing to obtain a first vehicle image with the maximum area index and a second vehicle image with the minimum aspect ratio index; and judging whether the absolute value of the difference value is smaller than a certain threshold value by calculating the absolute value of the difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index, if so, marking the first vehicle image as a vehicle weight-removing image, and if not, marking the first vehicle image and the second vehicle image as vehicle weight-removing images.
Description
Technical Field
The invention relates to the field of video image processing, in particular to a method and a device for eliminating vehicle weight with the maximum area and the minimum length-width ratio.
Background
In the video data processing, data processing methods meeting the requirements of different application scenes are developed according to specific service requirements, and vehicle weight removal is one of the methods applied to video structured processing. The vehicle weight removal is mainly applied to the weight removal of the same running or static vehicle in a video, and finally a vehicle image which is most consistent with a service scene is output, such as the clearest and most complete vehicle image. The vehicle weight elimination can be applied to the extraction of video vehicle structural information (vehicle color identification, vehicle type identification, license plate identification and the like), so that repeated vehicle data can be effectively reduced, the load of the rear end of the vehicle information extraction is reduced, and the performance of the device is greatly improved.
In the prior art, algorithms applied to vehicle weight reduction include a motion tracking algorithm and a vehicle feature extraction comparison algorithm, but the algorithms cannot provide a basis for obtaining a vehicle image which best meets a service scene, that is, the clearest and most complete vehicle image cannot be obtained from a video, so that the problems of omission, unclear vehicle image and the like are easily caused, and particularly, the clear and complete vehicle image cannot be obtained in a scene where a vehicle license plate is concerned more.
In view of the above, it is one of the problems to be solved urgently that a new vehicle weight elimination method is designed to obtain a clear and complete vehicle image.
Disclosure of Invention
The method aims at the problems that the vehicle weight removal cannot output the clearest and most complete vehicle image which is most consistent with a service scene, and longitudinal driving forms are easy to miss or the vehicle image is not clear and the like. An object of the embodiments of the present application is to provide a maximum area, minimum aspect ratio vehicle weight reduction method and apparatus to solve the technical problems mentioned in the background section above.
In a first aspect, an embodiment of the present application provides a maximum area and minimum aspect ratio vehicle weight reducing method, including the following steps:
s1: acquiring frame images in a video, and performing vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle;
s2: generating a vehicle ID in the vehicle image through a target tracking algorithm, and obtaining a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID;
s3: respectively calculating the area index and the aspect ratio index of a rectangular frame of each vehicle image in the vehicle image set, and comparing to obtain a first vehicle image with the maximum area index and a second vehicle image with the minimum aspect ratio index; and
s4: and judging whether the absolute value of the difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index is smaller than a certain threshold value, if so, marking the first vehicle image as a vehicle weight-removing image, and if not, marking the first vehicle image and the second vehicle image as vehicle weight-removing images.
In some embodiments, the target tracking algorithm comprises a DeepSORT algorithm. The deep SORT algorithm is an improved algorithm based on the SORT algorithm, can realize online tracking and judges whether the vehicles in the two vehicle images are the same vehicle.
In some embodiments, the area index S of the vehicle image is calculated in step S3 by 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 1 Coefficient, k, representing area index for reducing a rectangular frame when the rectangular frame is close to the edge of the vehicle image 1 Take 0.1. The image with the clearest and most complete vehicle can be obtained by calculating the area index with the largest vehicle image, and more characteristics about vehicle information can be obtained in the image.
In some embodiments, the aspect ratio index S of the vehicle image is calculated in step S3 by the following equation 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 2 Coefficient, k, representing the aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Take 0.1. The clearest image of the vehicle under the longitudinal driving posture can be obtained by calculating the minimum aspect ratio index of the vehicle image, and the clearest image of the vehicle license plate can be obtained under the scene where the vehicle license plate is concerned more.
In some embodiments, step S3 further comprises:
s31: if the calculated area index of the rectangular frame of the vehicle image of the vehicle ID has no history of the maximum area index, or the recorded maximum area index exists and the area index of the rectangular frame of the vehicle image is larger than the recorded maximum area index of the vehicle ID, updating the area index of the rectangular frame of the vehicle image to the maximum area index of the vehicle ID, and calculating the aspect ratio index of the rectangular frame of the vehicle image; s32: if the calculated aspect ratio index of the rectangular frame of the vehicle image has no history of the minimum aspect ratio index, or if there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID, the aspect ratio index of the rectangular frame of the vehicle image is updated to the minimum aspect ratio index of the vehicle ID.
The vehicle picture with the maximum area index and the vehicle picture with the minimum length-width ratio index can be accurately obtained through the steps, the vehicle is effectively arranged in a weight mode, the clearest and most complete vehicle images under the longitudinal driving posture are obtained, and the vehicle images under the longitudinal driving posture are not omitted.
In a second aspect, embodiments of the present application further provide a maximum area, minimum aspect ratio vehicle counterweight device, including:
the vehicle detection module is configured to acquire frame images in a video, and perform vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle;
the vehicle tracking module is configured to generate a vehicle ID in the vehicle image through a target tracking algorithm and obtain a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID;
the index calculation module is configured to calculate the area index and the aspect ratio index of the rectangular frame of each vehicle image in the vehicle image set respectively, and compare the area index and the aspect ratio index to obtain a first vehicle image with the largest area index and a second vehicle image with the smallest aspect ratio index; and
and the vehicle weight-removing module is configured to judge whether the absolute value of the difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index is smaller than a certain threshold value by calculating the absolute value of the difference value, mark the first vehicle image as a vehicle weight-removing image if the absolute value of the difference value is smaller than the certain threshold value, and mark the first vehicle image and the second vehicle image as vehicle weight-removing images if the absolute value of the difference value of the aspect ratio index of the rectangular frame in the first vehicle image is smaller than the certain threshold value.
In some embodiments, the target tracking algorithm comprises a DeepSORT algorithm. The deep SORT algorithm is an improved algorithm based on the SORT algorithm, can realize online tracking and judges whether the vehicles in the two vehicle images are the same vehicle.
In some embodiments, the area index S of the vehicle image is calculated in the index calculation module by the following formula 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 1 Coefficient, k, representing area index for reducing a rectangular frame when the rectangular frame is close to the edge of the vehicle image 1 Take 0.1. The image with the clearest and most complete vehicle can be obtained by calculating the area index with the largest vehicle image, and more characteristics about vehicle information can be obtained in the image.
In some embodiments, the index calculation module calculates the aspect ratio index S of the vehicle image by 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 2 Coefficient, k, representing the aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Take 0.1. The clearest image of the vehicle under the longitudinal driving posture can be obtained by calculating the minimum aspect ratio index of the vehicle image, and the clearest image of the vehicle license plate can be obtained under the scene where the vehicle license plate is concerned more.
In some embodiments, the index calculation module further comprises:
an area index updating module configured to update the area index of the rectangular frame of the vehicle image to the maximum area index of the vehicle ID and calculate an aspect ratio index of the rectangular frame of the vehicle image if the calculated area index of the rectangular frame of the vehicle image of the vehicle ID has no history of the maximum area index, or if the recorded maximum area index exists and the area index of the rectangular frame of the vehicle image is greater than the recorded maximum area index of the vehicle ID;
an aspect ratio index updating module configured to update the aspect ratio index of the rectangular frame of the vehicle image to the minimum aspect ratio index of the vehicle ID if the calculated aspect ratio index of the rectangular frame of the vehicle image has no history of the minimum aspect ratio index, or if there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID.
The vehicle picture with the maximum area index and the vehicle picture with the minimum length-width ratio index can be accurately obtained through the steps, the vehicle is effectively arranged in a weight mode, the clearest and most complete vehicle images under the longitudinal driving posture are obtained, and the vehicle images under the longitudinal driving posture are not omitted.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
The embodiment of the application discloses a method and a device for removing the weight of a vehicle with the largest area and the smallest length-width ratio. The embodiment of the application can effectively meet the situation that the vehicle needs images when the vehicle longitudinally runs under the scene that the license plate of the vehicle is concerned more. The vehicle weight removing method and the vehicle weight removing device can effectively reduce repeated vehicle data, reduce vehicle information extraction rear-end load and greatly improve system performance. The finally obtained vehicle weight-removing picture can be applied to extraction of video vehicle structural information, such as vehicle color recognition, vehicle type recognition, license plate recognition and the like.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an exemplary device architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a schematic flow diagram of a maximum area, minimum aspect ratio vehicle deduplication method of an embodiment of the present invention;
FIG. 3 is a flowchart illustrating step S3 of the maximum area, minimum aspect ratio vehicle counterweight method of an embodiment of the invention;
FIG. 4 is a schematic illustration of a maximum area, minimum aspect ratio vehicle weight rejection device in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device suitable for implementing an electronic apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings. 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 invention.
Fig. 1 illustrates an exemplary device architecture 100 to which the maximum area, minimum aspect ratio vehicle heaving method or maximum area, minimum aspect ratio vehicle heaving device of embodiments of the present application may be applied.
As shown in fig. 1, the apparatus architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various applications, such as data processing type applications, file processing type applications, etc., may be installed on the terminal apparatuses 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of software or software modules (e.g., software or software modules used to provide distributed services) or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a background data processing server that processes files or data uploaded by the terminal devices 101, 102, 103. The background data processing server can process the acquired file or data to generate a processing result.
It should be noted that the vehicle duplication elimination method with the maximum area and the minimum aspect ratio provided in the embodiment of the present application may be executed by the server 105, or may also be executed by the terminal devices 101, 102, and 103, and accordingly, the vehicle duplication elimination device with the maximum area and the minimum aspect ratio may be disposed in the server 105, or may also be disposed in the terminal devices 101, 102, and 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the processed data does not need to be acquired from a remote location, the above device architecture may not include a network, but only a server or a terminal device.
Fig. 2 illustrates a maximum area and minimum aspect ratio vehicle weight-removing method disclosed in the embodiment of the present application, including the following steps:
s1: frame images in the video are obtained, and vehicle detection is carried out on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle.
In a specific embodiment, a part of the acquired video is selected to be read, and frame images in the video are acquired, wherein the frame images can be sorted according to a certain sequence, and in a preferred embodiment, the frame images with a certain sequence are obtained by sorting according to time. And traversing all the frame images, if the frame images cannot be completely traversed, performing step S1, if the frame images cannot be completely traversed, traversing the recorded vehicle information of each vehicle ID, if the vehicle information is completely traversed, finishing the vehicle information traversing, and if the vehicle information is not completely traversed, performing step S3.
And performing target detection on each frame image through a Yolo algorithm to detect a target vehicle in the frame image. In a specific embodiment, the Yolo algorithm uses a convolutional network to extract features, and then uses a fully-connected layer to obtain a predicted value. The network structure refers to the GooLeNet model, which contains 24 convolutional layers and 2 fully-connected layers, and for the convolutional layers, 1x1 convolution is mainly used to make channle reduction, and then 3x3 convolution follows. For convolutional and fully-connected layers, the Leaky ReLU activation function is used: max (x, 0.1 x). But the last layer uses a linear activation function. In addition to adopting this structure above, a lightweight version of Fast Yolo, which uses only 9 convolutional layers and uses fewer convolutional kernels in the convolutional layers, can also be used. The final detection results in obtaining an image of the vehicle with the detected vehicle including the complete vehicle and framing the image with the complete vehicle with a rectangular frame. In addition, the Yolo algorithm includes Yolo-v1 algorithm and Yolo9000 algorithm, and in other alternative embodiments, other algorithms with the same function or similar functions may be adopted to meet the requirements of target detection or other specific service scenarios.
S2: and generating a vehicle ID in the vehicle image through a target tracking algorithm, and obtaining a vehicle image set of the vehicle in the frame image corresponding to the vehicle ID.
In a particular embodiment, the target tracking algorithm includes a DeepsORT algorithm. The deep SORT algorithm is an improved algorithm based on the SORT algorithm, can realize online tracking, judges whether vehicles in two vehicle images are the same vehicle or not, and realizes motion tracking. In other optional embodiments, other target tracking algorithms such as the SORT algorithm may also be selected for target tracking, as long as the requirements of the corresponding service scenarios can be met. And finally, obtaining the integral digital identification code of the vehicle, namely the vehicle ID, wherein the vehicle ID has uniqueness, and the vehicles with the same vehicle ID are represented as the same vehicle.
S3: and respectively calculating the area index and the aspect ratio index of the rectangular frame of each vehicle image in the vehicle image set, and comparing to obtain a first vehicle image with the maximum area index and a second vehicle image with the minimum aspect ratio index.
In a particular embodiment, the area index S of the vehicle image may be calculated by the following equation 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 The width of the rectangular frame, w the length of the vehicle image, h the width of the vehicle image, and b a constant representing the degree of the rectangular frame approaching the edge of the vehicle image, and in a general case, b may be 100.k is a radical of 1 Coefficient, k, representing area index for reducing a rectangular frame when the rectangular frame is close to the edge of the vehicle image 1 Take 0.1. Under the condition of x 0 < b or x 0 +w 0 > w-b or y 0 +h 0 In the case of > h-b, k 1 Coefficients may be used to reduce the area index of the vehicle image near the left, right, and bottom edges. At this time k 1 Less than 1, and may take on a value of 0.1 to further draw the area index difference between vehicles near the left, right, and lower edges and vehicles at other locations to screen out images of suitable vehicles located on the middle upper side. When the area index is calculated in this way, a vehicle image having the largest vehicle area can be calculated. The image with the clearest and most complete vehicle can be obtained by calculating the largest area index of the vehicle image, and the image can contain more characteristics about vehicle information. The larger the area index of the vehicle image is, the more complete and clearer the vehicle is.
In the detailed descriptionCalculating an aspect ratio index S of the vehicle image by the following equation 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 The width of the rectangular frame, w the length of the vehicle image, h the width of the vehicle image, and b a constant representing the degree of the rectangular frame approaching the edge of the vehicle image, and in a general case, b may be 100.k is a radical of 2 Coefficient, k, representing the aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Take 0.1. Under the condition of x 0 < b or x 0 +w 0 > w-b or y 0 +h 0 In the case of > h-b, k 2 Coefficients may be used to reduce the image of the vehicle near the left, right, and lower edges. At this time k 2 Less than 1, and may be 0.1, to further draw the aspect ratio indices of vehicles near the left, right, and lower edges from those at other locations to screen out images of suitable vehicles located above the middle. By calculating the aspect ratio index in this way, a vehicle image with the smallest vehicle aspect ratio can be calculated. The clearest image of the vehicle under the longitudinal driving posture can be obtained by calculating the minimum aspect ratio index of the vehicle image, and the clearest image of the vehicle license plate can be obtained under the scene where the vehicle license plate is concerned more. The smaller the aspect ratio index is, the more the vehicle tends to travel longitudinally, and the larger the aspect ratio index is, the more the vehicle tends to travel laterally. Only under the condition of complete longitudinal running, complete vehicle license plate information can be obtained, and the method is more beneficial to scenes in which relevant data of the vehicle license plate are concerned.
In a specific embodiment, as shown in fig. 3, step S3 further includes:
s31: if the area index of the rectangular frame of the vehicle image of the vehicle ID calculated does not have the maximum area index history, this indicates that the vehicle is a completely new vehicle that has just been captured, and the vehicle has not appeared before, so there is no history of the maximum area index. Or the recorded maximum area index exists and the area index of the rectangular frame of the vehicle image is larger than the recorded maximum area index of the vehicle ID, updating the area index of the rectangular frame of the vehicle image to the maximum area index of the vehicle ID, and calculating the aspect ratio index of the rectangular frame of the vehicle image;
s32: if the aspect ratio index of the rectangular frame of the calculated vehicle image has no history of the minimum aspect ratio index, this indicates that the vehicle is a brand new vehicle that has just been captured, and the vehicle has not appeared before, so there is no history of the minimum aspect ratio index. Or there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID, the aspect ratio index of the rectangular frame of the vehicle image is updated to the minimum aspect ratio index of the vehicle ID.
In this case, at least two vehicle images may be obtained, one being the vehicle image having the largest area index and the other being the vehicle image having the smallest aspect ratio index.
S4: and judging whether the absolute value of the difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index is smaller than a certain threshold value, if so, marking the first vehicle image as a vehicle weight-removing image, and if not, marking the first vehicle image and the second vehicle image as vehicle weight-removing images.
The vehicle picture with the maximum area index and the vehicle picture with the minimum length-width ratio index can be accurately obtained through the steps, the vehicle is effectively arranged in a weight mode, the clearest and most complete vehicle images under the longitudinal driving posture are obtained, and the vehicle images under the longitudinal driving posture are not omitted. The main problem of vehicle weight elimination is to extract a vehicle image which is most suitable for a service scene in the video structuring process, so that subsequent work such as target vehicle track tracking, vehicle information identification and the like can provide a meaningful vehicle image which can be referred to conveniently.
With further reference to fig. 4, as an implementation of the methods illustrated in the above figures, the present application provides an embodiment of a maximum area, minimum aspect ratio vehicle weight rejection apparatus, which corresponds to the embodiment of the method illustrated in fig. 2, and which is particularly applicable to various electronic devices.
The embodiment of the application specifically comprises the following steps:
the vehicle detection module 1 is configured to acquire frame images in a video, and perform vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle;
the vehicle tracking module 2 is configured to generate a vehicle ID in the vehicle image through a target tracking algorithm, and obtain a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID;
the index calculation module 3 is configured to calculate an area index and an aspect ratio index of a rectangular frame of each vehicle image in the vehicle image set respectively, and compare the area indexes and the aspect ratio indexes to obtain a first vehicle image with the largest area index and a second vehicle image with the smallest aspect ratio index; and
and the vehicle weight-removing module 4 is configured to judge whether the absolute value of the difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index is smaller than a certain threshold value by calculating the absolute value of the difference value, mark the first vehicle image as a vehicle weight-removing image if the absolute value of the difference value is smaller than the certain threshold value, and mark the first vehicle image and the second vehicle image as the vehicle weight-removing image if the absolute value of the difference value is not smaller than the certain threshold value.
In a particular embodiment, the target tracking algorithm includes a DeepsORT algorithm. The DeepSORT algorithm is an improved algorithm based on the SORT algorithm, can realize online tracking, and judges whether the vehicles in the two vehicle images are the same vehicle.
In a specific embodiment, the area index S of the vehicle image is calculated in the index calculation module 3 by the following formula 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 1 Coefficient, k, representing area index for reducing a rectangular frame when the rectangular frame is close to the edge of the vehicle image 1 0.1 is taken. The image with the clearest and most complete vehicle can be obtained by calculating the area index with the largest vehicle image, and more characteristics about vehicle information can be obtained in the image.
In a specific embodiment, the index calculation module 3 calculates the aspect ratio index S of the vehicle image by the following formula 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is taken as the origin, x 0 Is the abscissa, y, of the endpoint at the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of a rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of how close the rectangular frame is to the edge of the vehicle image, k 2 Coefficient, k, representing the aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Take 0.1. The clearest image of the vehicle under the longitudinal driving posture can be obtained by calculating the minimum aspect ratio index of the vehicle image, and the clearest image of the vehicle license plate can be obtained under the scene where the vehicle license plate is concerned more.
In a specific embodiment, the index calculation module 3 further includes:
an area index updating module configured to update the area index of the rectangular frame of the vehicle image to the maximum area index of the vehicle ID and calculate an aspect ratio index of the rectangular frame of the vehicle image if the calculated area index of the rectangular frame of the vehicle image of the vehicle ID has no history of the maximum area index, or if the recorded maximum area index exists and the area index of the rectangular frame of the vehicle image is greater than the recorded maximum area index of the vehicle ID;
an aspect ratio index updating module configured to update the aspect ratio index of the rectangular frame of the vehicle image to the minimum aspect ratio index of the vehicle ID if the calculated aspect ratio index of the rectangular frame of the vehicle image has no history of the minimum aspect ratio index, or if there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID.
By the steps, the vehicle picture with the largest area index and the vehicle picture with the smallest length-width ratio index can be accurately obtained, the vehicle is effectively subjected to weight removal, the clearest and most complete vehicle images under the longitudinal driving posture are obtained, and the vehicle images under the longitudinal driving posture are not omitted.
The embodiment of the application discloses a method and a device for removing the weight of a vehicle with the largest area and the smallest length-width ratio. The embodiment of the application can effectively meet the situation that the vehicle needs images when the vehicle longitudinally runs under the scene that the license plate of the vehicle is concerned more. The vehicle weight removing method and the vehicle weight removing device can effectively reduce repeated vehicle data, reduce vehicle information extraction rear-end load and greatly improve system performance. The finally obtained vehicle weight-removing picture can be applied to extraction of video vehicle structural information, such as vehicle color recognition, vehicle type recognition, license plate recognition and the like.
Referring now to fig. 5, a schematic diagram of a computer apparatus 500 suitable for implementing an electronic device (e.g., the server or the terminal device shown in fig. 1) according to an embodiment of the present application is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 5, the computer apparatus 500 includes a Central Processing Unit (CPU) 501 and a Graphics Processing Unit (GPU) 502, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 503 or a program loaded from a storage section 509 into a Random Access Memory (RAM) 504. In the RAM504, various programs and data necessary for the operation of the apparatus 500 are also stored. The CPU 501, GPU502, ROM 503, and RAM504 are connected to each other via a bus 505. An input/output (I/O) interface 506 is also connected to bus 505.
The following components are connected to the I/O interface 506: an input portion 507 including a keyboard, a mouse, and the like; an output section 508 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 509 including a hard disk and the like; and a communication section 510 including a network interface card such as a LAN card, a modem, or the like. The communication section 510 performs communication processing via a network such as the internet. Drivers 511 may also be connected to the I/O interface 506 as desired. A removable medium 512 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 511 as necessary, so that a computer program read out therefrom is mounted into the storage section 509 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications section 510, and/or installed from removable media 512. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 501 and a Graphics Processing Unit (GPU) 502.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a vehicle detection module, a vehicle tracking module, an index calculation module, and a vehicle weight removal module. The names of these modules do not constitute a limitation on the module itself in some cases, for example, the vehicle detection module may also be described as "being configured to acquire frame images in a video, and performing vehicle detection on each frame image through an object detection algorithm to obtain a vehicle image including a rectangular frame of a complete vehicle".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiment; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring frame images in a video, and performing vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle; generating a vehicle ID in the vehicle image through a target tracking algorithm, and obtaining a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID; respectively calculating the area index and the aspect ratio index of the rectangular frame of each vehicle image in the vehicle image set, and comparing to obtain a first vehicle image with the maximum area index and a second vehicle image with the minimum aspect ratio index; and calculating an absolute value of a difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index, and judging whether the absolute value of the difference value is smaller than a certain threshold value, if so, marking the first vehicle image as a vehicle weight-removing image, and if not, marking the first vehicle image and the second vehicle image as vehicle weight-removing images.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (3)
1. A maximum area, minimum aspect ratio vehicle weight rejection method, comprising the steps of:
s1: acquiring frame images in a video, and performing vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle;
s2: generating a vehicle ID in the vehicle image through a target tracking algorithm, and obtaining a vehicle image set of a vehicle in the frame image corresponding to the vehicle ID, wherein the target tracking algorithm comprises a DeepSORT algorithm;
s3: respectively calculating the area index and the aspect ratio index of the rectangular frame of each vehicle image in the vehicle image set, and comparing to obtain a first vehicle image with the maximum area index and a second vehicle image with the minimum aspect ratio index; and
s4: calculating an absolute value of a difference value between the aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index, and judging whether the absolute value of the difference value is smaller than a certain threshold value, if so, marking the first vehicle image as a vehicle weight-removing image, and if not, marking the first vehicle image and the second vehicle image as vehicle weight-removing images;
in the step S3, an area index S of the vehicle image is calculated by the following equation 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is used as an origin, x 0 Is the abscissa, y, of the endpoint of the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of the rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of the extent to which the rectangular frame is close to the edge of the vehicle image, b takes 100 1 Coefficient, k, representing an area index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 1 Taking 0.1 to screen out the clearest and most complete vehicle image of which the vehicle is positioned at the middle upper position of the image;
the aspect ratio index S of the vehicle image is calculated by the following equation in the step S3 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is used as an origin, x 0 Is the abscissa, y, of the endpoint of the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of the rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of the extent to which the rectangular frame is close to the edge of the vehicle image, b takes 100 2 A coefficient, k, representing an aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Taking 0.1 to screen out the vehicle image which is positioned at the middle upper position of the image and under the longitudinal driving posture;
the step S3 further includes:
s31: if the calculated area index of the rectangular frame of the vehicle image of the vehicle ID has no history of the maximum area index, or there is a recorded maximum area index and the area index of the rectangular frame of the vehicle image is greater than the recorded maximum area index of the vehicle ID, updating the area index of the rectangular frame of the vehicle image to the maximum area index of the vehicle ID, and calculating an aspect ratio index of the rectangular frame of the vehicle image;
s32: if the calculated aspect ratio index of the rectangular frame of the vehicle image has no history of the minimum aspect ratio index, or there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID, the aspect ratio index of the rectangular frame of the vehicle image is updated to the minimum aspect ratio index of the vehicle ID.
2. A maximum area, minimum aspect ratio vehicle weight management apparatus, comprising:
the vehicle detection module is configured to acquire frame images in the video, and perform vehicle detection on each frame image through a Yolo algorithm to obtain a vehicle image containing a rectangular frame of a complete vehicle;
the vehicle tracking module is configured to generate a vehicle ID in the vehicle image through a target tracking algorithm, and obtain a vehicle image set of a vehicle corresponding to the vehicle ID in the frame image, wherein the target tracking algorithm comprises a DeepsORT algorithm;
the index calculation module is configured to calculate an area index and an aspect ratio index of the rectangular frame of each vehicle image in the vehicle image set respectively, and compare the area index and the aspect ratio index to obtain a first vehicle image with the largest area index and a second vehicle image with the smallest aspect ratio index; and
a vehicle weight-removing module configured to determine whether an absolute value of a difference between an aspect ratio index of the rectangular frame in the first vehicle image and the minimum aspect ratio index is smaller than a certain threshold by calculating the absolute value of the difference, if so, mark the first vehicle image as a vehicle weight-removing image, and if not, mark the first vehicle image and the second vehicle image as vehicle weight-removing images;
the index calculation module calculates an area index S of the vehicle image by the following formula 1 ：
Wherein, the endpoint of the upper left corner of the vehicle image is used as an origin, x 0 Is the abscissa, y, of the endpoint of the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of the rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of the extent to which the rectangular frame is close to the edge of the vehicle image, b takes 100 1 Indicating that the rectangular frame is used for zooming out when the rectangular frame is close to the edge of the vehicle imageCoefficient of area index, k, of the rectangular box 1 Taking 0.1 to screen out the vehicle image which is located at the middle upper position of the image and is clearest and most complete;
the index calculation module calculates an aspect ratio index S of the vehicle image by the following formula 2 ：
Wherein, the endpoint of the upper left corner of the vehicle image is used as an origin, x 0 Is the abscissa, y, of the endpoint of the upper left corner of the rectangular frame 0 Is the ordinate, w, of the endpoint at the upper left corner of the rectangular frame 0 Is the length of the rectangular frame, h 0 Is the width of the rectangular frame, w is the length of the vehicle image, h is the width of the vehicle image, b represents a constant of the extent to which the rectangular frame is close to the edge of the vehicle image, b takes 100 2 A coefficient, k, representing an aspect ratio index for reducing the rectangular frame when the rectangular frame is close to the edge of the vehicle image 2 Taking 0.1 to screen out the vehicle image which is positioned at the middle upper position of the image and under the longitudinal driving posture;
the index calculation module further comprises:
an area index updating module configured to update an area index of the rectangular frame of the vehicle image of the vehicle ID to a maximum area index of the vehicle ID and calculate an aspect ratio index of the rectangular frame of the vehicle image if the calculated area index of the rectangular frame of the vehicle image of the vehicle ID has no maximum area index history or there is a recorded maximum area index and the area index of the rectangular frame of the vehicle image is greater than the recorded maximum area index of the vehicle ID;
an aspect ratio index updating module configured to update the aspect ratio index of the rectangular frame of the vehicle image to the minimum aspect ratio index of the vehicle ID if the calculated aspect ratio index of the rectangular frame of the vehicle image has no history of the minimum aspect ratio index, or there is a recorded minimum aspect ratio index and the aspect ratio index of the rectangular frame of the vehicle image is smaller than the recorded minimum aspect ratio index of the vehicle ID.
3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of claim 1.
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