CN112200148B - Decentralizing traffic integrity evaluation system - Google Patents
Decentralizing traffic integrity evaluation system Download PDFInfo
- Publication number
- CN112200148B CN112200148B CN202011229779.6A CN202011229779A CN112200148B CN 112200148 B CN112200148 B CN 112200148B CN 202011229779 A CN202011229779 A CN 202011229779A CN 112200148 B CN112200148 B CN 112200148B
- Authority
- CN
- China
- Prior art keywords
- module
- vehicle
- traffic
- video
- illegal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title abstract description 8
- 230000006399 behavior Effects 0.000 claims abstract description 51
- 238000012545 processing Methods 0.000 claims abstract description 30
- 238000013528 artificial neural network Methods 0.000 claims abstract description 22
- 238000004891 communication Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 20
- 230000008859 change Effects 0.000 claims description 17
- 230000009471 action Effects 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 14
- 238000013527 convolutional neural network Methods 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 10
- 230000007812 deficiency Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 5
- 230000007246 mechanism Effects 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims 2
- 239000011521 glass Substances 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 13
- 238000012549 training Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 238000005452 bending Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Multimedia (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a decentralization traffic integrity evaluation system which consists of a vehicle-mounted recording module, a server judging module, an APP integrity inquiring module and a traffic management network, wherein the vehicle-mounted recording module comprises a power supply module, a main control module, a video capturing module, a video processing module, a 4G communication module and a GPS positioning module; in the driving process, the video processing module judges whether traffic illegal behaviors exist in the front and rear vehicles through videos captured by the video capturing module, the illegal videos are uploaded to the server judging module through the 4G communication module, the server judging module further judges and reports the illegal videos to the traffic management network by means of the neural network, and meanwhile the APP integrity inquiring module displays illegal information to generate traffic integrity scores for vehicle owners. The invention automatically identifies the traffic illegal behaviors of the front and rear vehicles in the driving process, so that the supervision of the traffic illegal behaviors is not executed by specific crowd or equipment, the decentralization of traffic management is realized, and the restraint force of traffic law enforcement is increased.
Description
Technical Field
The invention belongs to the technical field of traffic management, and particularly relates to a decentralised traffic integrity evaluation system.
Background
In the future, the industrial scale of smart cities will be continuously expanding, and intelligent transportation and public safety will be a key field of smart cities. The frequent occurrence of some traffic illegal behaviors which are not easy to be supervised, such as solid line lane change behaviors, seriously threatens public safety, and proves that certain loopholes exist in the existing traffic supervision system. The traditional electronic police cameras are fixed in position, limited in quantity and small in coverage range, and traffic illegal actions of avoiding the cameras by drivers often occur, so that the traffic illegal actions in the monitoring blind areas are frequent.
At present, most of monitoring and reporting of traffic illegal behaviors are captured by means of fixed cameras arranged at intersections, or a reporting system for capturing and uploading traffic illegal behaviors by a mobile phone of a reporter witness the traffic illegal behaviors to a traffic management network. Such supervision and reporting is not only inefficient, has a small coverage, but also lacks sufficient evidence, and lacks deterrence to illegal vehicles. Traffic illegal actions such as unbelted safety belts, too close vehicle distances and the like frequently occur on special road sections such as highways and the like which lack fixed cameras, so that huge potential safety hazards are caused, and a more sound traffic illegal action supervision system is urgently needed for supervision.
CN110136447a discloses a method for detecting lane change of driving and identifying illegal lane change, which comprises the following steps: (1) Acquiring a real-time video image of a road section to be monitored by a monitoring camera of the road section to be monitored; (2) Marking and storing all lane line information to be detected of the road section; (3) And identifying and positioning all vehicles in the real-time video, marking and capturing a screenshot for evidence of the vehicles judged to generate the solid lane change, and recording license plate information of the vehicles. The method has obvious defects, still needs to use a monitoring camera at a fixed position, has larger dead angle and has smaller coverage area.
CN111047875a discloses a system and method for rapidly reporting traffic offence during driving, the system comprises the following steps: (1) Before driving, fixing the mobile phone in the vehicle, shooting the position in front of driving and shooting in real time; (2) Storing video information of 10 seconds before and after reporting in real time as reporting evidence; (3) When the driver finds out the traffic illegal action in front, the reporting function is started through the sliding screen or voice control, and the system judges whether the traffic illegal action belongs to or not through the video information and uploads the traffic illegal action to the traffic police reporting platform. However, this system still requires the driver to manually judge whether there is traffic violation in front, has a certain hysteresis, and has traffic hidden trouble.
Disclosure of Invention
The invention aims to provide a decentralised traffic integrity evaluation system which realizes the functions of automatically identifying and reporting traffic illegal behaviors of vehicles in front and behind in the running process of the vehicles and evaluating traffic integrity of drivers with traffic illegal behaviors.
The technical solution for realizing the purpose of the invention is as follows: a decentralised traffic integrity evaluation system comprises a vehicle-mounted recording module, a server judging module, an APP integrity inquiring module and a traffic management network.
The vehicle-mounted recording module comprises: the system comprises a power supply module, a main control module, a video capturing module, a video processing module, a 4G communication module and a GPS positioning module; the power module, the video capturing module, the video processing module, the 4G communication module and the GPS positioning module are respectively connected with the main control module through circuits; the power supply module is connected with an automobile power supply circuit, and the automobile power supply provides voltage for the power supply module; the GPS positioning module reads the positioning of the vehicle in real time, and divides the position of the vehicle into two conditions of a highway and a common road according to the positioning; the video processing module judges whether the vehicle runs on the expressway according to the positioning information of the GPS positioning module, then carries out image processing on the video captured by the video capturing module frame by frame, and judges whether traffic illegal behaviors exist on the front and rear vehicles; the 4G communication module is in communication connection with the server judgment module through a 4G network, and uploads the video with traffic illegal behaviors to the server judgment module.
The server judging module is in communication connection with the traffic management network, further judges whether traffic illegal behaviors exist in the video by means of a neural network algorithm, and uploads the video with the traffic illegal behaviors and license plate information of illegal vehicles to the traffic management network; the neural network used by the server judging module comprises: the system comprises a vehicle identification GoogleNET network, a license plate number CNN network, a signal lamp fast RCNN network, a lane line identification SSD network and a safety belt identification DBN network.
The APP integrity query module is in communication connection with the server judgment module, and synchronizes the traffic illegal behaviors judged by the server judgment module into the APP; the APP honest query module comprises: a registration interface, a login interface, a query interface and an intelligent contract interface; the vehicle owner fills in the identity information such as the driving license number, the name, the identity card number, the license plate number and the like through a registration interface to register, and uses the identity card number and the password to register on a registration interface; the APP honest inquiring module generates traffic honest scores for the vehicle owners according to the traffic illegal behaviors of the vehicle owners, and the vehicle owners can inquire the traffic illegal behaviors, the illegal behavior videos and the traffic honest scores of the vehicle owners after logging in the inquiring interface; the APP intelligent contract interface has the following functions: when the owner applies for renting and sharing the automobile, becomes a taxi driver, becomes a pilot driver and even inquires personal credit information, the corresponding mechanism is authorized to inquire the traffic integrity score of the owner and contract the owner on an intelligent contract interface, and the APP integrity inquiry module assists the mechanism to automatically release the contract with the owner when the integrity score of the owner is reduced to a certain value.
Preferably, the video capturing module includes: the first camera, the second camera, the third camera and the alarm bell; the first camera is arranged on the right side of the left headlight of the vehicle and faces the front of the vehicle, the second camera is arranged on the left side of the right headlight of the vehicle and faces the front of the vehicle, the third camera is arranged on the roof and faces the rear of the vehicle, and the third camera is connected with the alarm bell circuit; the video processing module judges whether the front vehicle has traffic illegal behaviors according to the image information of the first camera and the second camera, and judges whether the rear vehicle has traffic illegal behaviors according to the image information acquired by the third camera.
Preferably, the specific steps of the video processing module for processing video are as follows:
(1) Identifying lane lines and judging the deficiency and the excess of the lane lines
Converting the video into an image frame, cutting the lower half part of the image frame, carrying out graying, noise filtering, extracting edges, respectively carrying out binarization according to a white lane line and a yellow lane line selection threshold value to obtain a black and white color block, and then obtaining the position and the slope (curvature) of the lane line through a Hough transform algorithm and a conical curve fitting algorithm. And then judging the deficiency and the excess of the lane line by a duty ratio calculation method.
(2) Identifying the position of a vehicle and a license plate
Graying the image frame, filtering noise, extracting edges, and positioning the vehicle position according to the contour features of the vehicle body for the front vehicle; for a rear vehicle, the vehicle position is located according to the contour features of the windshield. And then binarizing according to a threshold value selected by the blue license plate and the yellow license plate, eliminating an interference color block according to the similarity between the outline of the license plate and the rectangle, positioning the license plate position, and proving that the vehicle has no license plate or is shielded if the license plate is not detected.
(3) Judging whether the front vehicle is subjected to solid line illegal lane change
Judging whether the front vehicle changes lanes or not according to the relative position relation between the front vehicle and the lane lines, and judging whether to press lines or not according to the intersecting relation between the contour edge of the front vehicle body and the lane lines. And judging whether the vehicle performs the solid line illegal lane change according to the deficiency and the excess of the lane line.
(4) Judging whether the front vehicle runs the red light
When the GPS positioning module judges that a vehicle runs on a common highway, the upper half part of an image frame, namely an area where a traffic signal lamp is located, is cut, subjected to grey scale, noise filtering and edge extraction, respectively subjected to binarization according to the color of the signal lamp, eliminating interference color blocks according to the similarity between the outline and the circle of the signal lamp, determining the position and the color of the signal lamp, and judging whether the front vehicle runs red light traffic illegal behaviors according to the relative position change of the front vehicle and the color of the signal lamp; if the signal lamp is not detected, the red light running judgment is not carried out.
(5) Judging whether the distance between the rear vehicles is too close
When the GPS positioning module judges that the vehicle runs on the expressway, firstly, the contour features of the rear windshield are identified, the distance between the rear vehicle and the vehicle is judged according to the change of the relative positions of the rear vehicle and the vehicle, after the distance is continuously smaller than the safety distance for a period of time, the rear vehicle is judged to have traffic illegal behaviors that the distance between the vehicles on the expressway is too close, and the main control module controls the warning bell length and howling warning of the roof.
(6) Judging whether the driver and the copilot of the rear vehicle wear the safety belt
The phenomenon that the expressway driver and the copilot do not wear safety belts is still common, and a large potential safety hazard is generated; when the GPS positioning module judges that the vehicle is near a toll of a highway (the vehicle distance is relatively close, and the video acquired by the roof camera is relatively clear), the video processing module starts to judge whether the rear vehicle driver and the copilot wear the safety belt. Firstly, according to the size and the position of a rear windshield contour, a driver picture frame and a co-driver picture frame are cut in an image frame, gray-scale treatment, noise filtering and edge extraction are respectively carried out, the position of a human face is judged according to the contour characteristics of the human face, the position of the human face is taken as a reference, shoulder contours are extracted, and whether a safety belt is worn or not is judged through comparison of the lengths of the left shoulder contour and the right shoulder contour.
Compared with the prior art, the invention has the beneficial effects that: the video capturing module is arranged on a running vehicle, so that the defect that the traditional electronic police can only be fixed at a certain position is overcome, the supervision blind area is greatly reduced, and a stronger deterrent effect can be generated for traffic offenders; the system automatically performs video capturing and judgment of traffic illegal behaviors, does not need drivers to participate in the reporting process, and reduces the driving safety hidden trouble; the system is arranged at a fixed position of the vehicle body, so that the system is convenient to adjust and maintain; the server judging module carries out second auditing on the reported video through a neural network algorithm, so that the accuracy of reported information is greatly improved, and the traffic illegal action processing pressure of a traffic management network is reduced; the APP integrity query module can query whether the driver has traffic illegal behaviors or not in real time, and query the traffic integrity score of the driver, so that the supervision and management of the driver are increased.
Drawings
Fig. 1 is a block diagram of a decentralised traffic integrity assessment system according to the present invention.
Fig. 2 is a block diagram of the vehicle-mounted recording module according to the present invention.
Fig. 3 is a block diagram of a server judgment module according to the present invention.
Fig. 4 is a block diagram of the APP integrity query module of the present invention.
Detailed Description
The present invention is further illustrated below in conjunction with specific embodiments, it being understood that the embodiments are merely illustrative of the present invention and not limiting the scope of the present invention, and that modifications of various equivalent forms of the present invention will fall within the scope of the appended claims to the person skilled in the art after reading the present invention.
As shown in fig. 1, the invention discloses a decentralised traffic integrity evaluation system, which comprises a vehicle-mounted recording module 1, a server judging module 2, an APP integrity query module 3 and a traffic management network 4. When a private car is purchased, hung or maintained, or a taxi works with public vehicles such as buses, the vehicle-mounted recording module 1 is mounted on the car by a traffic police, and the traffic police department can give proper assistance to the car on which the module is mounted and regularly maintain and upgrade the module; the server judging module 2 is in communication connection with a database of the traffic management network, further judges whether traffic illegal behaviors exist in the video by means of a neural network algorithm, reduces false alarm, and uploads the video with the traffic illegal behaviors and license plate information of illegal vehicles to the traffic management network to complete the reporting process; the APP integrity query module 3 is in communication connection with the server judging module 2, the traffic illegal actions judged by the server judging module 2 are synchronized into the APP, an owner of the APP completes the APP identity registration by providing information such as own driving license, license plate number and the like, the module generates traffic integrity scores for the owners according to the traffic illegal actions of the owners, and the owners can query own traffic illegal information and illegal videos at the APP.
As shown in fig. 2, the in-vehicle recording module 1 includes: the system comprises a power supply module 11, a main control module 12, a video capturing module 13, a video processing module 14, a 4G communication module 15 and a GPS positioning module 16; the power module 11, the video capturing module 13, the video processing module 14, the 4G communication module 15 and the GPS positioning module 16 are respectively connected with the main control module 12 in a circuit manner; the power supply module 11 is connected with an automobile power supply circuit, and the automobile power supply supplies voltage to the power supply module 11; the GPS positioning module 16 reads the positioning of the vehicle in real time, and divides the position of the vehicle into two conditions of expressways and common roads according to the positioning; the video processing module 14 judges whether the vehicle runs on the expressway according to the positioning information of the GPS positioning module 16, then carries out image processing on the video captured by the video capturing module 13 frame by frame, and judges whether traffic illegal behaviors exist in the front and rear vehicles; the 4G communication module 15 is in communication connection with the server judgment module 2 through a 4G network, and uploads the video with traffic illegal behaviors to the server judgment module 2.
As shown in fig. 2, the video capturing module 13 includes: the first camera 131, the second camera 132, the third camera 133 and the alarm bell 134; the first camera 131 is arranged on the right side of the left headlight of the vehicle and faces the front of the vehicle, the second camera 132 is arranged on the left side of the right headlight of the vehicle and faces the front of the vehicle, the third camera 133 is arranged on the roof of the vehicle and faces the rear of the vehicle, and the third camera 133 is in circuit connection with the alarm bell 134; the video processing module 14 determines whether the front vehicle has traffic law enforcement according to the image information of the first camera 131 and the second camera 132, and determines whether the rear vehicle has traffic law enforcement according to the image information collected by the third camera 133.
The specific steps of the video processing module 14 for processing video are as follows:
(1) Identifying lane lines and judging the deficiency and the excess of the lane lines
The lane lines are divided into straight lines and curved lines. The identification of the straight line is based on the hough transform. The basic principle is that the point-to-line duality is utilized to convert the straight line of the original image space into a point of the Hough parameter space, and the problem of detecting the straight line in the original image is converted into the problem of searching the peak value in the parameter space. The method comprises the steps of converting video into image frames, cutting the lower half part (part containing lane lines) of the image frames, respectively carrying out graying, noise filtering, edge extraction, respectively carrying out binarization according to a white lane line and a yellow lane line selection threshold value to obtain a plurality of black-white straight line segments, obtaining the intercept and the slope of each straight line segment through a Hough transform algorithm, and screening the position of the lane line according to the slope characteristics of the lane line.
The identification of the curve line is realized based on a conic curve fitting algorithm. Because the curve can be regarded as an elliptical shape locally, a conic curve can be used for fitting, as follows: firstly converting video into image frames, respectively carrying out graying, image filtering and edge extraction on an effective area, respectively carrying out binarization according to a white lane line and a yellow lane line selection threshold value to obtain black-white color blocks containing curve segments, respectively solving parameters of a conic curve by using points of each color block area, substituting points of the remaining color blocks into an equation of the conic curve, and enabling points of which the equation is established within a certain error to be regarded as points on the curved line, wherein the conic curve with the largest point set is the position corresponding to the curved line.
After the positions of the lane lines are determined, the reality of the lane lines is judged by a method of calculating the black-white duty ratio of the lane lines after binarization, the lane lines with duty ratio close to 1 are solid lines, and the lane lines with duty ratio close to 0.5 are broken lines.
(2) Identifying the position of a vehicle and a license plate
Graying the image frame, filtering noise, extracting edges, and positioning the vehicle position according to the contour features of the vehicle body for the front vehicle; for a rear vehicle, the vehicle position is located according to the contour features of the windshield. And then binarizing according to a threshold value selected by the blue license plate and the yellow license plate, eliminating an interference color block according to the similarity between the outline of the license plate and the rectangle, positioning the license plate position, and proving that the vehicle has no license plate or is shielded if the license plate is not detected.
(3) Judging whether the front vehicle is subjected to solid line illegal lane change
Judging whether the front vehicle changes lanes or not according to the relative position relation between the front vehicle and the lane lines, and judging whether to press lines or not according to the intersecting relation between the contour edge of the front vehicle body and the lane lines. And judging whether the vehicle performs the solid line illegal lane change according to the deficiency and the excess of the lane line.
(4) Judging whether the front vehicle runs the red light
When the GPS positioning module 16 determines that the vehicle is traveling on a common road, the upper half of the image frame, that is, the area where the traffic signal is located, is cut, and is subjected to graying, noise filtering, edge extraction, binarization is respectively performed by adopting three thresholds according to the color (red, yellow and green) of the signal, interference color blocks are eliminated according to the similarity between the contour of the signal and the circle, whether the signal is present is determined, and the position and color of the signal are determined. Judging whether the front vehicle runs the traffic law according to the relative position change of the front vehicle and the color of the signal lamp; if the signal lamp is not detected, the red light running judgment is not carried out.
(5) Judging whether the distance between the rear vehicles is too close
The motor vehicle should be kept at a distance of at least 50 meters or more from the same lane vehicle when traveling on the highway. When the GPS positioning module 16 judges that the vehicle runs on the expressway, firstly, the outline features of the rear windshield are identified, the distance between the rear vehicle and the vehicle is judged according to the change of the relative positions of the rear vehicle and the vehicle, after the distance is continuously less than 50 meters and reaches 20 seconds, the traffic illegal action that the vehicle distance on the expressway is too close is judged to occur on the rear vehicle, and the main control module controls the warning bell and the howling warning on the roof.
(6) Judging whether the driver and the copilot of the rear vehicle wear the safety belt
The phenomenon that the expressway driver and the copilot do not wear safety belts is still common, and a large potential safety hazard is generated. When the GPS positioning module 16 determines that the vehicle is near a toll of a highway (where the vehicle distance is relatively short and the video captured by the roof camera is relatively clear), the video processing module begins to determine whether the rear driver and the copilot wear a safety belt. According to the size and the position of the contour of the rear windshield, a driver picture frame and a co-driver picture frame are cut in an image frame, gray-scale treatment, noise filtering and edge extraction are respectively carried out, the position of a human face is judged according to the contour characteristics of the human face, the position of the human face is taken as a reference, shoulder contours are extracted, and whether a safety belt is worn or not is judged through comparison of the lengths of the left shoulder contour and the right shoulder contour.
As shown in fig. 3, the server judging module 2 adopts a neural network algorithm to identify traffic illegal actions in the video, and the used neural network includes: the system comprises a recognition vehicle GoogleNET network (21), a recognition license plate number CNN network (22), a recognition signal lamp Faster RCNN network (23), a recognition lane line SSD network (24) and a recognition safety belt DBN network (25). The training period of the neural network is longer, so that the module adopts a neural network model trained in advance and does not update online.
The training of the recognition vehicle GoogleNET network (21) is based on an N-CARS data set, pictures of the front and the back of the vehicle in the data set are screened as a training set, pictures of the side face of the vehicle are removed, a GoogleNET network model in a CNN neural network is selected for training, the angle of a camera is used, pictures of roadside vehicles are collected as a test set, and the network is evaluated.
The training of the recognition license plate number CNN network (22) is based on the CCPD data set, and the CNN neural network is selected because the CNN neural network can integrally process the whole image frame, and the segmentation of license plate numbers is avoided. The training process firstly cuts license plate photos of the data set, separates out single numbers and places the single numbers in different categories. CNN neural networks were trained using 34 different classes of data sets (26 capital English letters with "I" and "O" removed and numbers 0-10), and evaluated using test sets
The training for identifying the signal lamp Faster RCNN network (23) is based on a data set of 'Traffic Lights Recognition (TLR) public benchmarks', and the training is performed by selecting the Faster RCNN neural network in the CNN neural network in consideration of the fact that the signal lamp has a small target in a picture frame, and the neural network is more advantageous in the aspect of detecting the small target. And collecting pictures of signal lamps in different weather to form a test set, and evaluating the network.
The training of the SSD network (24) for identifying the lane lines is based on a data set formed by pictures of the lane lines collected by various surrounding environments on the network, the SSD neural network in the CNN neural network is considered to be advantageous in processing linear characteristics, the SSD neural network is used for identifying the straight lane lines and the curved lane lines, smaller detection frames are selected as much as possible according to the characteristics of the lane lines, the straight lane lines and the curved lane lines are identified and marked in blocks, and the bending, straight, virtual and actual of the lane lines can be judged according to the slope and the intensity of the blocks.
The training of the identification safety belt DBN network (25) is based on a data set formed by pictures collected on the network, and in order to remove invalid areas, the picture set of the part below the neck of a driver and a copilot is selected as a training set, and the training set is divided into two types of wearing safety belts and not wearing safety belts; firstly, mapping feature vectors of training set pictures to different feature spaces by adopting an RBM network, realizing layer-by-layer learning of features, and reserving feature information of images as much as possible; then, by means of BP network as the last layer of DBN network, the RBM network is received to output characteristic vector, and the whole DBN network is finely tuned in a supervision manner; when the network is tested, firstly, a haar feature detection algorithm is carried out on a driver picture frame and a co-driver picture frame in the picture to detect the face features, and the picture frame of the part below the neck is selected as a test object to be input into the network, and the network is evaluated.
The operation process of the server judging module 2 is as follows: when the server judging module 2 receives the illegal video information sent by the vehicle-mounted recording module 1, the image frames of the video are respectively imported into each network for identification, whether traffic illegal behaviors exist or not is judged, and the video information with the traffic illegal behaviors and the identified license plate information are uploaded to the traffic management network 4 to complete the reporting process.
As shown in fig. 4, the APP integrity query module 3 includes a registration interface 31, a login interface 32, a query interface 33, and an intelligent contract interface 34. The vehicle owner fills in the identity information such as the driving license number, the name, the identity card number, the license plate number and the like through the registration interface 31 to register, the registration interface 32 uses the identity card number and the password to register, the query interface 33 can query own traffic illegal behaviors and related illegal video evidence after the registration, and when the situation of false report occurs, the vehicle owner can conduct complaint on the query interface 33, and the manual background can screen and cancel the traffic illegal behaviors with false judgment. The APP integrity query module 3 generates traffic integrity scores according to traffic violations of vehicle owners, and can be checked at the query interface 33. The APP smart contract interface 34 has the following functions: when the owner applies for renting the shared automobile, becomes a taxi driver, becomes a pilot driver and even inquires personal credit information, the corresponding organization has the right to inquire the traffic integrity score of the owner and contract the intelligent contract interface 34, and the APP integrity inquiry module 3 assists the organization to automatically release the contract with the owner when the integrity score of the owner falls to a certain value.
In summary, the invention discloses a decentralised traffic integrity evaluation system, and mainly describes the composition and the operation process of the system. First, the vehicle-mounted recording module is mounted on a private car by a traffic police when the private car is purchased, registered or maintained, or when a taxi and a public car such as a bus are operated. In the driving process, after the video processing module carries out image processing on the video captured by the video capturing module frame by frame, judging whether traffic illegal behaviors such as solid-line illegal lane changing, red light running, no license plate or license plate exist on a front vehicle or not according to the image information acquired by the two cameras in front of the vehicle; according to the vehicle rear image information collected by the vehicle roof camera, judging whether a driver or a copilot does not wear a safety belt, the vehicle distance is too close, no license plate or license plate exists in the rear vehicle, uploading a video containing the illegal behaviors to a server judging module through a 4G communication module, then further judging whether the traffic illegal behaviors exist in the video by the server judging module through a neural network, finding out corresponding vehicle owner information according to license plate numbers, displaying the illegal information to the vehicle owner at an APP honest inquiring module, uploading the video information and license plate information with the traffic illegal behaviors to a traffic management network, completing the reporting process, and successfully completing the reported vehicle owner to obtain reporting rewards of traffic management authorities as incentives for supervising the traffic illegal behaviors. Compared with the prior system, the system is arranged on a private car and other travelling crane, greatly reduces the supervision blind area, can generate stronger deterrence effect on traffic offenders, and simultaneously automatically performs video capturing and judgment of traffic offender behaviors without the participation of drivers in the reporting process, thereby reducing the travelling safety hidden trouble. The system is arranged at a fixed position of the vehicle body, so that the adjustment and the maintenance are convenient, the server judging module carries out second auditing on the reported video through a neural network algorithm, the accuracy of reported information is greatly improved, and the traffic illegal action processing pressure of the traffic management network is reduced. The APP integrity query module can query whether the driver has traffic illegal behaviors or not in real time, and query the traffic integrity score of the driver, so that the supervision and management of the driver are increased. The invention makes the supervision of traffic illegal actions no longer executed by specific crowd (traffic police) or equipment (electronic police), realizes the decentralization of traffic supervision, and increases the range and restriction force of traffic law enforcement.
Claims (1)
1. A decentralised traffic integrity assessment system, comprising: the system comprises a vehicle-mounted recording module (1), a server judging module (2), an APP integrity query module (3) and a traffic management network (4);
the vehicle-mounted recording module (1) comprises: the system comprises a power supply module (11), a main control module (12), a video capturing module (13), a video processing module (14), a 4G communication module (15) and a GPS positioning module (16); the power module (11), the video capturing module (13), the video processing module (14), the 4G communication module (15) and the GPS positioning module (16) are respectively connected with the main control module (12) in a circuit manner; the power supply module (11) is connected with an automobile power supply circuit, and the automobile power supply supplies voltage to the power supply module (11); the GPS positioning module (16) reads the positioning of the vehicle in real time, and divides the position of the vehicle into two conditions of a highway and an ordinary road according to the positioning; the video processing module (14) judges whether the vehicle runs on the expressway according to the positioning information of the GPS positioning module (16), then carries out image processing on the video captured by the video capturing module (13) frame by frame, and judges whether traffic illegal behaviors exist in the front and rear vehicles; the 4G communication module (15) is in communication connection with the server judgment module (2) through a 4G network, and uploads a video with traffic illegal behaviors to the server judgment module (2);
the video capturing module (13) includes: the first camera (131), the second camera (132), the third camera (133) and the alarm bell (134); the first camera (131) is arranged on the right side of a left headlight of the vehicle and faces the front of the vehicle, the second camera (132) is arranged on the left side of the right headlight of the vehicle and faces the front of the vehicle, the third camera (133) is arranged on the roof of the vehicle and faces the rear of the vehicle, and the third camera (133) is in circuit connection with the alarm bell (134); the video processing module (14) judges whether the front vehicle has traffic illegal behaviors according to the image information of the first camera (131) and the second camera (132), and judges whether the rear vehicle has traffic illegal behaviors according to the image information acquired by the third camera (133), wherein the traffic illegal behaviors of the front vehicle comprise whether the front vehicle has a solid line illegal lane change, red light running, no license or license shielding, and the traffic illegal behaviors of the rear vehicle comprise whether the driver or the copilot does not tie a safety belt, the distance between vehicles is too close, no license or license shielding;
the specific steps of the video processing module (14) for processing the video are as follows:
(1) Identifying lane lines and judging the deficiency and the excess of the lane lines
The lane lines are divided into a straight lane line and a curve line; the identification of the straight line is realized based on Hough transformation, and specifically comprises the steps of firstly converting a video into an image frame, cutting the lower half part of the image frame, respectively carrying out graying, noise filtering, edge extraction, respectively carrying out binarization according to a white lane line and a yellow lane line selection threshold value to obtain a plurality of black-white straight line segments, obtaining the intercept and the slope of each straight line segment through a Hough transformation algorithm, and screening the position of the lane line according to the slope characteristic of the lane line, wherein the lower half part of the image frame comprises the lane line part;
the curve line identification is realized based on a conic curve fitting algorithm, and the steps are as follows: firstly converting video into image frames, respectively carrying out graying, image filtering and edge extraction on an effective area, respectively carrying out binarization according to a white lane line and a yellow lane line selection threshold value to obtain black-white color blocks containing curve segments, respectively solving parameters of a conic curve by using points of each color block area, substituting points of the remaining color blocks into an equation of the conic curve, wherein the points which enable the equation to be established within a certain error can be regarded as points on the curved line, and the conic curve with the largest point set is the position corresponding to the curved line;
after the position of the lane line is determined, judging the false and true of the lane line by a method of calculating the black-white duty ratio of the lane line after binarization, wherein the lane line with the duty ratio close to 1 is a solid line, and the lane line with the duty ratio close to 0.5 is a dotted line;
(2) Identifying the position of a vehicle and a license plate
Graying the image frame, filtering noise, extracting edges, and positioning the vehicle position according to the contour features of the vehicle body for the front vehicle; for a rear vehicle, positioning the vehicle position according to the contour features of the windshield; then, binarizing is carried out according to a threshold value selected by the blue license plate and the yellow license plate, interference color blocks are eliminated according to the similarity between the outline of the license plate and the rectangle, the license plate position is positioned, and if no license plate is detected, no license plate or no license plate is proved;
(3) Judging whether the front vehicle is subjected to solid line illegal lane change
Judging whether the front vehicle changes lanes or not according to the relative position relation between the front vehicle and the lane lines, and judging whether to press lines or not according to the intersection relation between the contour edge of the front vehicle body and the lane lines; judging whether the vehicle performs the solid line illegal lane change according to the deficiency and the excess of the lane line;
(4) Judging whether the front vehicle runs the red light
When the GPS positioning module judges that the vehicle runs on a common highway, the upper half part of the image frame, namely the area where the traffic signal lamp is located, is cut, grey-scale, noise filtering and edge extraction are carried out, binarization is respectively carried out according to the color of the signal lamp by adopting three thresholds, interference color blocks are eliminated according to the similarity between the outline and the circle of the signal lamp, whether the signal lamp exists or not is judged, and the position and the color of the signal lamp are determined; judging whether the front vehicle runs the traffic law according to the relative position change of the front vehicle and the color of the signal lamp; if the signal lamp is not detected, judging that the signal lamp runs the red light;
(5) Judging whether the distance between the rear vehicles is too close
When the motor vehicle runs on the expressway, the motor vehicle should keep a distance of at least 50 meters from the same lane vehicle; when the GPS positioning module judges that the vehicle runs on the expressway, firstly, the outline characteristics of the wind shielding glass of the rear vehicle are identified, the distance between the two vehicles is judged according to the change of the relative positions of the rear vehicle and the vehicle, after the distance is continuously less than 50 meters and reaches 20 seconds, the traffic illegal action that the vehicle distance on the expressway is too close is judged to occur on the rear vehicle, and the main control module controls the alarm bell length howling warning of the roof;
(6) Judging whether the driver and the copilot of the rear vehicle wear the safety belt
According to the size and the position of the contour of the rear windshield, a driver picture frame and a copilot picture frame are cut in an image frame, gray-scale treatment, noise filtering and edge extraction are respectively carried out, the position of a human face is judged according to the contour characteristics of the human face, the position of the human face is taken as a reference, shoulder contours are extracted, and whether a safety belt is worn or not is judged through comparison of the lengths of the left shoulder contour and the right shoulder contour;
the server judging module (2) is in communication connection with the traffic management network (4), and is used for further judging whether traffic illegal behaviors exist in the video based on a neural network algorithm, and uploading the video with the traffic illegal behaviors and license plate information of illegal vehicles to the traffic management network (4); the neural network used by the server judging module (2) comprises: the method comprises the steps of identifying a vehicle GoogleNET network (21), a license plate number CNN network (22), a signal lamp fast RCNN network (23), a lane line SSD network (24) and a safety belt DBN network (25);
the APP integrity query module (3) is in communication connection with the server judgment module (2) and synchronizes the traffic illegal behaviors judged by the server judgment module (2) into the APP; the APP honest query module (3) comprises: a registration interface (31), a login interface (32), a query interface (33) and an intelligent contract interface (34); the vehicle owner fills in the driving license number, the name, the identity card number and the license plate number through a registration interface (31) for registration, and uses the identity card number and the password for registration at a registration interface (32); the APP integrity query module (3) generates traffic integrity scores for the vehicle owners according to the traffic illegal behaviors of the vehicle owners, and the vehicle owners can query own traffic illegal behaviors, illegal behavior videos and traffic integrity scores at the query interface (33) after logging in; the APP intelligent contract interface (34) has the following functions: when the owner applies for renting and sharing the automobile, becomes a taxi driver, becomes a pilot driver and even inquires personal credit information, the corresponding mechanism is authorized to inquire the traffic integrity score of the owner and contract the owner at an intelligent contract interface (34), and the APP integrity inquiry module (3) assists the mechanism to automatically release the contract with the owner when the integrity score of the owner falls to a certain value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011229779.6A CN112200148B (en) | 2020-11-06 | 2020-11-06 | Decentralizing traffic integrity evaluation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011229779.6A CN112200148B (en) | 2020-11-06 | 2020-11-06 | Decentralizing traffic integrity evaluation system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112200148A CN112200148A (en) | 2021-01-08 |
| CN112200148B true CN112200148B (en) | 2024-01-26 |
Family
ID=74034114
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011229779.6A Active CN112200148B (en) | 2020-11-06 | 2020-11-06 | Decentralizing traffic integrity evaluation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112200148B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114299472A (en) * | 2021-12-23 | 2022-04-08 | 上海闪马智能科技有限公司 | Method and device for determining abnormal behavior, storage medium and electronic device |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202584417U (en) * | 2012-05-17 | 2012-12-05 | 西安亿力通微波科技有限公司 | Expressway traffic management and guidance command system based on mobile communication |
| CN103065470A (en) * | 2012-12-18 | 2013-04-24 | 浙江工业大学 | Detection device for behaviors of running red light of vehicle based on machine vision with single eye and multiple detection faces |
| CN103568991A (en) * | 2012-08-02 | 2014-02-12 | 上海晨兴希姆通电子科技有限公司 | Auxiliary driving safety system and method |
| CN104008645A (en) * | 2014-06-12 | 2014-08-27 | 湖南大学 | Lane line predicating and early warning method suitable for city road |
| CN104537841A (en) * | 2014-12-23 | 2015-04-22 | 上海博康智能信息技术有限公司 | Unlicensed vehicle violation detection method and detection system thereof |
| CN105946786A (en) * | 2016-05-06 | 2016-09-21 | 厦门理工学院 | Detection method for standard wearing of safety belt, reminding system for standard wearing of safety belt and control method for reminding system |
| CN107248293A (en) * | 2017-07-28 | 2017-10-13 | 陕西中熙汇天网络科技有限公司 | Traffic offence mobile monitor analysis of information collection method and its system |
| CN107481526A (en) * | 2017-09-07 | 2017-12-15 | 公安部第三研究所 | System and method for drive a vehicle lane change detection record and lane change violating the regulations report control |
| CN107679520A (en) * | 2017-10-30 | 2018-02-09 | 湖南大学 | A kind of lane line visible detection method suitable for complex condition |
| CN108528448A (en) * | 2017-03-02 | 2018-09-14 | 比亚迪股份有限公司 | Vehicle travels autocontrol method and device |
| CN108961766A (en) * | 2018-07-21 | 2018-12-07 | 浙江天地人科技有限公司 | Running red light for vehicle method for detecting and system |
| CN109214336A (en) * | 2018-09-05 | 2019-01-15 | 高新兴科技集团股份有限公司 | A kind of vehicle window marker detection method and device |
| CN109741484A (en) * | 2018-12-24 | 2019-05-10 | 南京理工大学 | Automobile data recorder and its working method with image detection and voice alarm function |
| CN110135318A (en) * | 2019-05-08 | 2019-08-16 | 佳都新太科技股份有限公司 | Cross determination method, apparatus, equipment and the storage medium of vehicle record |
| CN110182168A (en) * | 2019-06-03 | 2019-08-30 | 福建省汽车工业集团云度新能源汽车股份有限公司 | A kind of adaptive security band regulating system and automobile |
| CN110197589A (en) * | 2019-05-29 | 2019-09-03 | 杭州诚道科技股份有限公司 | A kind of illegal detection method of making a dash across the red light based on deep learning |
| CN111860610A (en) * | 2020-06-29 | 2020-10-30 | 高新兴科技集团股份有限公司 | License plate overexposure and license plate vehicle identification method and device and computer storage medium |
-
2020
- 2020-11-06 CN CN202011229779.6A patent/CN112200148B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202584417U (en) * | 2012-05-17 | 2012-12-05 | 西安亿力通微波科技有限公司 | Expressway traffic management and guidance command system based on mobile communication |
| CN103568991A (en) * | 2012-08-02 | 2014-02-12 | 上海晨兴希姆通电子科技有限公司 | Auxiliary driving safety system and method |
| CN103065470A (en) * | 2012-12-18 | 2013-04-24 | 浙江工业大学 | Detection device for behaviors of running red light of vehicle based on machine vision with single eye and multiple detection faces |
| CN104008645A (en) * | 2014-06-12 | 2014-08-27 | 湖南大学 | Lane line predicating and early warning method suitable for city road |
| CN104537841A (en) * | 2014-12-23 | 2015-04-22 | 上海博康智能信息技术有限公司 | Unlicensed vehicle violation detection method and detection system thereof |
| CN105946786A (en) * | 2016-05-06 | 2016-09-21 | 厦门理工学院 | Detection method for standard wearing of safety belt, reminding system for standard wearing of safety belt and control method for reminding system |
| CN108528448A (en) * | 2017-03-02 | 2018-09-14 | 比亚迪股份有限公司 | Vehicle travels autocontrol method and device |
| CN107248293A (en) * | 2017-07-28 | 2017-10-13 | 陕西中熙汇天网络科技有限公司 | Traffic offence mobile monitor analysis of information collection method and its system |
| CN107481526A (en) * | 2017-09-07 | 2017-12-15 | 公安部第三研究所 | System and method for drive a vehicle lane change detection record and lane change violating the regulations report control |
| CN107679520A (en) * | 2017-10-30 | 2018-02-09 | 湖南大学 | A kind of lane line visible detection method suitable for complex condition |
| CN108961766A (en) * | 2018-07-21 | 2018-12-07 | 浙江天地人科技有限公司 | Running red light for vehicle method for detecting and system |
| CN109214336A (en) * | 2018-09-05 | 2019-01-15 | 高新兴科技集团股份有限公司 | A kind of vehicle window marker detection method and device |
| CN109741484A (en) * | 2018-12-24 | 2019-05-10 | 南京理工大学 | Automobile data recorder and its working method with image detection and voice alarm function |
| CN110135318A (en) * | 2019-05-08 | 2019-08-16 | 佳都新太科技股份有限公司 | Cross determination method, apparatus, equipment and the storage medium of vehicle record |
| CN110197589A (en) * | 2019-05-29 | 2019-09-03 | 杭州诚道科技股份有限公司 | A kind of illegal detection method of making a dash across the red light based on deep learning |
| CN110182168A (en) * | 2019-06-03 | 2019-08-30 | 福建省汽车工业集团云度新能源汽车股份有限公司 | A kind of adaptive security band regulating system and automobile |
| CN111860610A (en) * | 2020-06-29 | 2020-10-30 | 高新兴科技集团股份有限公司 | License plate overexposure and license plate vehicle identification method and device and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112200148A (en) | 2021-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11074813B2 (en) | Driver behavior monitoring | |
| CN106652468B (en) | The detection and from vehicle violation early warning alarm set and method in violation of rules and regulations of road vehicle front truck | |
| US11380105B2 (en) | Identification and classification of traffic conflicts | |
| US11836985B2 (en) | Identifying suspicious entities using autonomous vehicles | |
| KR102282800B1 (en) | Method for trackig multi target employing ridar and camera | |
| CN108932849B (en) | Method and device for recording low-speed running illegal behaviors of multiple motor vehicles | |
| KR102174556B1 (en) | Apparatus for monitoring image to control traffic information employing Artificial Intelligence and vehicle number | |
| CN115035491A (en) | Driving behavior road condition early warning method based on federal learning | |
| CN110782677A (en) | Illegal vehicle snapshot warning method and device | |
| CN113676702A (en) | Target tracking monitoring method, system and device based on video stream and storage medium | |
| CN109670431A (en) | A kind of behavioral value method and device | |
| CN113469105A (en) | Method and device for monitoring surrounding vehicle and computer readable storage medium | |
| CN114387785A (en) | Safety management and control method and system based on intelligent highway and storable medium | |
| CN108932850B (en) | Method and device for recording low-speed driving illegal behaviors of motor vehicle | |
| CN112200148B (en) | Decentralizing traffic integrity evaluation system | |
| CN113870551B (en) | Road side monitoring system capable of identifying dangerous and non-dangerous driving behaviors | |
| CN113076852A (en) | Vehicle-mounted snapshot processing system occupying bus lane based on 5G communication | |
| CN118119543A (en) | Driver state evaluation method, device, electronic equipment and storage medium | |
| CN115019516A (en) | Automobile violation reporting system based on camera identification and millimeter wave radar detection | |
| JP7207912B2 (en) | Driving evaluation system | |
| CN113177443A (en) | Method for intelligently identifying road traffic violation based on image vision | |
| Tech et al. | Automatic Traffic E Challan Generation using Deep learning | |
| CN114973655B (en) | Intelligent traffic real-time alarm system based on mobile internet | |
| TWI786725B (en) | Method and system for detecting and processing traffic violations | |
| Tsai | Development of a Sensing Methodology for Intelligent and Reliable Work-Zone Hazard Awareness |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |