Truck red light running evidence obtaining and correcting method based on road monitoring video

Abstract

The invention relates to the technical field of image recognition, and particularly discloses a truck red light running evidence obtaining and correcting method based on a road monitoring video, and the method comprises the steps: obtaining the road monitoring video in real time; when a truck runs in the road monitoring video, the position of the virtual stop line is automatically adjusted, and after the signal lamp is switched to be red, a first truck running image, a second truck running image and a third truck running image are sequentially shot; when the first truck driving image and the second truck driving image are both unqualified, correcting the first truck driving image and the second truck driving image; and taking the corrected first truck driving image and the corrected second truck driving image as a first illegal evidence image and a second illegal evidence image respectively. The method can improve the evidence obtaining success rate of red light running of the truck, inhibits the illegal behavior of red light running of the truck through strengthening afterwards punishment, and effectively reduces the traffic accident rate of the truck.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and more specifically to a method for collecting and correcting evidence of a large truck running a red light based on a road monitoring video. Background Art

[0002] The off-site evidence collection of large trucks running red lights follows standards such as the "Technical Specifications for Image Evidence Collection of Road Traffic Safety Violations" and the "General Technical Conditions for Automatic Red Light Running Recording Systems". Generally, at least three photos of the violation are required. When the traffic light ahead is red, the front wheels of the vehicle cross the stop line and the first photo is taken. When the rear wheels cross the stop line again, another photo is taken. The third photo is taken when the vehicle reaches the zebra crossing or has passed the zebra crossing.

[0003] The automatic red light running recording system has a high accuracy rate in capturing evidence photos for general small and medium-sized vehicles. However, for large trucks running red lights, the photos captured are not very accurate due to the following reasons.

[0004] First, when a large truck runs a red light, it is easy to trigger the camera before the front wheels cross the stop line.

[0005] Existing electronic police systems generally trigger photography based on video. The specific method is to draw a virtual stop line along the actual stop line in the image, and trigger photography when part of the vehicle touches the virtual stop line.

[0006] Since the virtual stop line is set according to the height of most small and medium-sized vehicles, and large trucks are tall, it is easy to trigger the photo in advance when shooting from the rear. At this time, the front wheels of the large truck may not have crossed the stop line, resulting in insufficient evidence.

[0007] Second, the rear compartments of some large trucks are too high and too large, blocking the front wheels and making it impossible to determine whether the front wheels have crossed the line.

[0008] Some large-scale gated trucks and vans carry bulky goods, and the electronic police at the intersection are not installed at a high height and the shooting angle is from the rear. The carriage and goods in the image will block the front wheels of the truck. In addition, the shooting starts when the truck just crosses the stop line. It is difficult to determine whether the front wheels of the truck have crossed the line based on the photo alone.

[0009] Third, some large trucks have relatively long bodies, which resulted in the rear wheels not passing the stop line when the second photo was taken.

[0010] After the electronic police for running a red light takes the first picture, the first method is to delay the second picture for a certain period of time according to experience. The second method is to start taking the second picture when there are no more vehicles at the virtual stop line. The third method is to set a virtual starting line in front of the stop line and snap the second picture when the vehicle passes by. For the first method, because the body of the large truck is relatively long, the delay time is not enough, and the rear wheels have not passed the stop line, resulting in insufficient evidence. For the second method, due to algorithm problems, the image of the stop line area needs to remain unchanged for a period of time to determine that no vehicles have passed, which will cause delays in capturing; and if there are other vehicles behind the large truck that are too close to the vehicle, it may result in missed shots. For the third method, also because the body of the large truck is relatively long, when the front of the truck reaches the virtual starting line of the rear wheel, the rear wheel has not passed the stop line, resulting in the second photo being captured in advance.

[0011] Due to the above reasons, the evidence pictures of large trucks running red lights collected by electronic police at off-site sites are very likely to cause controversy, which makes it difficult to punish large trucks running red lights, reduces the deterrent effect on large trucks breaking the law, and increases safety hazards. Therefore, a method for correcting evidence collection of large trucks running red lights is urgently needed. Summary of the invention

[0012] In order to solve the deficiencies in the prior art, the present invention provides a method for correcting evidence of large trucks running red lights based on road monitoring videos, which can improve the success rate of evidence collection of large trucks running red lights, curb the illegal behavior of large trucks running red lights by strengthening post-event penalties, and effectively reduce the incidence of large truck traffic accidents on national, provincial and urban roads.

[0013] As a first aspect of the present invention, a method for collecting and correcting evidence of a large truck running a red light based on a road monitoring video is provided, comprising the following steps: Step S1: Acquire road surveillance video of the intersection in real time; Step S2: when a large truck is traveling in the road monitoring video, the position of the virtual stop line in the road monitoring video is automatically adjusted, and after the traffic light in front of the large truck is switched to a red light, the first large truck driving image, the second large truck driving image and the third large truck driving image are sequentially photographed; wherein, the first large truck driving image is photographed when the front wheels of the large truck pass through the virtual stop line, the second large truck driving image is photographed when the rear wheels of the large truck pass through the virtual stop line, and the third large truck driving image is photographed when the large truck reaches the zebra crossing position or passes the zebra crossing position, and the third large truck driving image is used as the third large truck driving image as the third large truck running red light violation evidence image; Step S3: when both the first large truck driving image and the second large truck driving image are unqualified, the first large truck driving image and the second large truck driving image are corrected to obtain a corrected first large truck driving image and a corrected second large truck driving image; Step S4: using the first corrected image of the truck driving as the first image of evidence of the truck running a red light violation, and using the second corrected image of the truck driving as the second image of evidence of the truck running a red light violation.

[0014] Furthermore, the step S3 and the step S4 further include: When both the first large truck driving image and the second large truck driving image are unqualified, identifying the driving track of the large truck in the road monitoring video; When the driving track of the large truck in the road monitoring video intersects the virtual stop line, intercepting the current driving image of the large truck in the road monitoring video, and using the current driving image of the large truck in the road monitoring video as the first evidence image of the large truck running a red light violation, so as to realize the correction of the first driving image of the large truck; Intercepting multiple frames of large truck driving images between the first large truck red light violation evidence image and the third large truck red light violation evidence image from the road monitoring video; Selecting a current middle frame of the truck driving image from the plurality of truck driving images by a binary method; and identifying whether the rear wheel of the truck in the current middle frame of the truck driving image is located on the virtual stop line; If the rear wheel of the truck in the current intermediate frame of the truck driving image is located on the virtual stop line, the current intermediate frame of the truck driving image is used as the second evidence image of the truck running a red light violation; If the rear wheels of the truck in the current intermediate frame truck driving image are not located on the virtual stop line, the next intermediate frame truck driving image is selected backward or forward by binary selection, and this cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line, and the next intermediate frame truck driving image is used as the second evidence image of the truck running a red light violation, so as to correct the second truck driving image.

[0015] Furthermore, if the rear wheels of the truck in the current intermediate frame truck driving image are not located on the virtual stop line, then the next intermediate frame truck driving image is selected by a backward or forward dichotomy method, further comprising: If the rear wheel of the truck in the current intermediate frame truck driving image is in front of the virtual stop line, then the next intermediate frame truck driving image is selected by backward dichotomy, and it is identified whether the rear wheel of the truck in the next intermediate frame truck driving image is located on the virtual stop line, and this cycle is repeated until the rear wheel of the truck in the next intermediate frame truck driving image is located on the virtual stop line; If the rear wheels of the truck in the current intermediate frame truck driving image are behind the virtual stop line, the next intermediate frame truck driving image is selected by forward dichotomy, and it is identified whether the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line. This cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line.

[0016] Furthermore, when both the first truck driving image and the second truck driving image are unqualified, identifying the driving track of the truck in the road monitoring video further includes: When both the first truck driving image and the second truck driving image are unqualified, a multi-target tracking algorithm is applied to track the truck in the road monitoring video, and the driving trajectory of the truck is identified in combination with a video target recognition algorithm.

[0017] The method for correcting evidence of large trucks running red lights based on road monitoring videos provided by the present invention has the following advantages: through refined analysis of illegal videos of large trucks running red lights, red light running evidence photos that meet the requirements are intercepted to correct the large trucks running red light violations evidence pictures, thereby improving the success rate of large trucks running red light evidence collection, and by strengthening post-event penalties to curb illegal red light running by large trucks, the incidence of large truck traffic accidents on national, provincial and urban roads is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0019] Figure 1 A flow chart of a method for collecting and correcting evidence of a large truck running a red light based on road monitoring video provided by the present invention.

[0020] Figure 2 A flowchart of a specific implementation method of a method for collecting and correcting evidence of a large truck running a red light based on road monitoring video provided by the present invention. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of a large truck running a red light evidence correction method based on road monitoring video proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] In this embodiment, a method for collecting and correcting evidence of a large truck running a red light based on road monitoring video is provided. Figure 1 As shown, the method for collecting and correcting evidence of a large truck running a red light based on road monitoring video includes: Step S1: Acquire road surveillance video of the intersection in real time; It should be noted that the road surveillance video is an electronic police video, which is connected to the electronic police video through an interface module and pre-processed, such as image denoising and resolution adjustment.

[0025] Step S2: when a large truck is traveling in the road monitoring video, the position of the virtual stop line in the road monitoring video is automatically adjusted, and after the traffic light in front of the large truck is switched to a red light, the first large truck driving image, the second large truck driving image and the third large truck driving image are sequentially photographed; wherein, the first large truck driving image is photographed when the front wheels of the large truck pass through the virtual stop line, the second large truck driving image is photographed when the rear wheels of the large truck pass through the virtual stop line, and the third large truck driving image is photographed when the large truck reaches the zebra crossing position or passes the zebra crossing position, and the third large truck driving image is used as the third large truck driving image as the third large truck running red light violation evidence image; It should be noted that the target recognition algorithm is embedded in the electronic police red light running capture algorithm, and the target recognition algorithm is used to identify in real time whether the vehicle approaching the virtual stop line in the road surveillance video is a large truck. When the vehicle approaching the virtual stop line in the road surveillance video is identified as a large truck, the position of the virtual stop line in the road surveillance video is automatically adjusted, thereby triggering the red light running photo at the appropriate virtual stop line position.

[0026] Step S3: when both the first large truck driving image and the second large truck driving image are unqualified, the first large truck driving image and the second large truck driving image are corrected to obtain a corrected first large truck driving image and a corrected second large truck driving image; It should be noted that the off-site evidence image review algorithm that supports the identification of disputed evidence images is used to pre-review the first large truck driving image and the second large truck driving image. When both the first large truck driving image and the second large truck driving image are unqualified, it is necessary to correct the first large truck driving image and the second large truck driving image. When both the first large truck driving image and the second large truck driving image are qualified, the first large truck driving image and the second large truck driving image are used as the first large truck driving image and the second large truck driving image, respectively. Evidence image of the illegal red light violation.

[0027] Step S4: using the first corrected image of the truck driving as the first image of evidence of the truck running a red light violation, and using the second corrected image of the truck driving as the second image of evidence of the truck running a red light violation.

[0028] Preferably, Figure 2 As shown, the step S3 and step S4 further include: When both the first large truck driving image and the second large truck driving image are unqualified, identifying the driving track of the large truck in the road monitoring video; It should be noted that when both the first large truck driving image and the second large truck driving image are unqualified, the corresponding historical road surveillance video is retrieved from the electronic police for analysis.

[0029] Specifically, when both the first large truck driving image and the second large truck driving image are unqualified, identifying the driving track of the large truck in the road monitoring video further includes: When both the first truck driving image and the second truck driving image are unqualified, a multi-target tracking algorithm (such as Deep SORT) is applied to track the truck in the road monitoring video, and the driving trajectory of the truck is identified in combination with a video target recognition algorithm.

[0030] When the driving track of the large truck in the road monitoring video intersects the virtual stop line, intercepting the current driving image of the large truck in the road monitoring video, and using the current driving image of the large truck in the road monitoring video as the first evidence image of the large truck running a red light violation, so as to realize the correction of the first driving image of the large truck; It should be noted that this method is applicable to situations where the electronic police has captured a truck running a red light, but the first evidence photo is controversial because it was taken too early. Because the vehicle trajectory is tracked based on the center point of the vehicle, when the truck trajectory intersects the virtual stop line, it means that the middle of the vehicle has crossed the stop line. At this time, the front wheels of the truck must have crossed the stop line, and the rear wheels of the truck have not yet passed the stop line. At this time, the captured picture is more in line with the requirements of the first evidence image of the truck running a red light violation, and it is not easy to cause controversy.

[0031] In order to ensure the validity of the first image of evidence of the large truck running a red light violation, a red light running review algorithm may be used to review the first image of evidence of the large truck running a red light violation.

[0032] In case that the second truck driving image is unqualified, multiple frames of truck driving images between the first truck running a red light violation evidence image and the third truck running a red light violation evidence image are intercepted from the road monitoring video according to the timestamp; It should be noted that the image segment between the first image of evidence of a large truck running a red light violation and the third image of evidence of a large truck running a red light violation in the road surveillance video is framed second by second, and for vehicles with faster speeds, frames are extracted at shorter intervals.

[0033] Selecting a current middle frame of the truck driving image from the plurality of truck driving images by a binary method; and identifying whether the rear wheel of the truck in the current middle frame of the truck driving image is located on the virtual stop line; If the rear wheel of the truck in the current intermediate frame of the truck driving image is located on the virtual stop line, the current intermediate frame of the truck driving image is used as the second evidence image of the truck running a red light violation; If the rear wheels of the truck in the current intermediate frame truck driving image are not located on the virtual stop line, the next intermediate frame truck driving image is selected backward or forward by binary selection, and this cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line, and the next intermediate frame truck driving image is used as the second evidence image of the truck running a red light violation, so as to correct the second truck driving image.

[0034] It should be noted that the image of a truck's rear wheels passing the stop line has obvious features, such as the visible features of the truck's rear end and the stop line. Therefore, the truck's rear wheel crossing line recognition algorithm can be trained by collecting sample images to identify whether the truck's rear wheels in the image are crossing the line.

[0035] Preferably, Figure 2 As shown, if the rear wheel of the truck in the current intermediate frame truck driving image is not located on the virtual stop line, then the next intermediate frame truck driving image is selected backward or forward by binary division, and further includes: If the rear wheels of the truck in the current intermediate frame of the truck driving image are in front of the virtual stop line (the rear wheels of the truck have not crossed the line), then the next intermediate frame of the truck driving image is selected by backward dichotomy, and it is identified whether the rear wheels of the truck in the next intermediate frame of the truck driving image are located on the virtual stop line, and this cycle is repeated until the rear wheels of the truck in the next intermediate frame of the truck driving image are located on the virtual stop line; If the rear wheels of the truck in the current intermediate frame truck driving image are behind the virtual stop line (the rear wheels of the truck have passed the line but are too far away from the stop line), the next intermediate frame truck driving image is selected by forward dichotomy, and it is identified whether the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line, and this cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line.

[0036] The present invention provides a method for correcting evidence of large trucks running red lights based on road monitoring videos. The method performs a refined analysis on illegal videos of large trucks running red lights, captures red light running evidence photos that meet the requirements, and corrects the large trucks running red light violations evidence pictures, thereby improving the success rate of large trucks running red light evidence collection. By strengthening post-event penalties to curb illegal red light running by large trucks, the incidence of large truck traffic accidents on national, provincial, and urban roads is effectively reduced.

[0037] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for collecting and correcting evidence of a large truck running a red light based on road monitoring video, characterized in that: The method for collecting and correcting evidence of a large truck running a red light based on a road monitoring video comprises the following steps: Step S1: Acquire road surveillance video of the intersection in real time; Step S2: when a large truck is traveling in the road monitoring video, the position of the virtual stop line in the road monitoring video is automatically adjusted, and after the traffic light in front of the large truck is switched to a red light, the first large truck driving image, the second large truck driving image and the third large truck driving image are sequentially photographed; wherein, the first large truck driving image is photographed when the front wheels of the large truck pass through the virtual stop line, the second large truck driving image is photographed when the rear wheels of the large truck pass through the virtual stop line, and the third large truck driving image is photographed when the large truck reaches the zebra crossing position or passes the zebra crossing position, and the third large truck driving image is used as the third large truck driving image as the third large truck running red light violation evidence image; Step S3: when both the first large truck driving image and the second large truck driving image are unqualified, the first large truck driving image and the second large truck driving image are corrected to obtain a corrected first large truck driving image and a corrected second large truck driving image; Step S4: using the first corrected image of the truck driving as the first image of evidence of the truck running a red light violation, and using the second corrected image of the truck driving as the second image of evidence of the truck running a red light violation.

2. The method for collecting and correcting evidence of a large truck running a red light based on road monitoring video according to claim 1 is characterized in that: The steps S3 and S4 further include: When both the first large truck driving image and the second large truck driving image are unqualified, identifying the driving track of the large truck in the road monitoring video; When the driving track of the large truck in the road monitoring video intersects the virtual stop line, intercepting the current driving image of the large truck in the road monitoring video, and using the current driving image of the large truck in the road monitoring video as the first evidence image of the large truck running a red light violation, so as to realize the correction of the first driving image of the large truck; Intercepting multiple frames of large truck driving images between the first large truck red light violation evidence image and the third large truck red light violation evidence image from the road monitoring video; Selecting a current middle frame of the truck driving image from the plurality of truck driving images by a binary method; and identifying whether the rear wheel of the truck in the current middle frame of the truck driving image is located on the virtual stop line; If the rear wheel of the truck in the current intermediate frame of the truck driving image is located on the virtual stop line, the current intermediate frame of the truck driving image is used as the second evidence image of the truck running a red light violation; If the rear wheels of the truck in the current intermediate frame truck driving image are not located on the virtual stop line, the next intermediate frame truck driving image is selected backward or forward by binary selection, and this cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line, and the next intermediate frame truck driving image is used as the second evidence image of the truck running a red light violation, so as to correct the second truck driving image.

3. The method for collecting and correcting evidence of a large truck running a red light based on road monitoring video according to claim 2 is characterized in that: If the rear wheels of the truck in the current intermediate frame truck driving image are not located on the virtual stop line, then the next intermediate frame truck driving image is selected by a backward or forward dichotomy method, further comprising: If the rear wheel of the truck in the current intermediate frame truck driving image is in front of the virtual stop line, then the next intermediate frame truck driving image is selected by backward dichotomy, and it is identified whether the rear wheel of the truck in the next intermediate frame truck driving image is located on the virtual stop line, and this cycle is repeated until the rear wheel of the truck in the next intermediate frame truck driving image is located on the virtual stop line; If the rear wheels of the truck in the current intermediate frame truck driving image are behind the virtual stop line, the next intermediate frame truck driving image is selected by forward dichotomy, and it is identified whether the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line. This cycle is repeated until the rear wheels of the truck in the next intermediate frame truck driving image are located on the virtual stop line.

4. The method for collecting and correcting evidence of a large truck running a red light based on road monitoring video according to claim 2 is characterized in that: When both the first truck driving image and the second truck driving image are unqualified, identifying the driving track of the truck in the road monitoring video further includes: When both the first truck driving image and the second truck driving image are unqualified, a multi-target tracking algorithm is applied to track the truck in the road monitoring video, and the driving trajectory of the truck is identified in combination with a video target recognition algorithm.

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