CN104408942A - Intelligent vehicle speed measuring device and method - Google Patents
Intelligent vehicle speed measuring device and method Download PDFInfo
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Abstract
Disclosed an intelligent vehicle speed measuring device and method. The intelligent vehicle speed measuring device comprises an image collecting module, a storage module, a display module, a video processing module and an alarming module. The image collecting module collects colored video image information and transmits the same to the storage module, the display module and the video processing module at the same time, the display module displays actual highway traffic information in real time, the storage module is used for storing realtime video information for archiving and recording, and the video processing module performs speed measuring detection on the video information, detects to acquire vehicle overspeed information, displays and alarms through the steps of background modeling, front-ground detection, speed measuring line setting, vehicle information matching and updating, overspeed detection and alarming. By the intelligent vehicle speed measuring device and method, reliable vehicle speed measuring and realtime alarming can be realized, traffic managing personnel is assisted to timely handle overspeed conditions, traffic accidents are prevented, and traffic safety is guaranteed intelligently while traffic order is maintained.
Description
Technical field
The invention belongs to intelligent video analysis monitoring field, relate to the technology such as image procossing, video analysis, pattern-recognition, intelligent transportation, artificial intelligence.
Background technology
Along with the development of China's means of transportation is perfect, highway extends to all parts of the country, and China's number of vehicles is also in quick growth.But, in vehicle travel process, be easy to hypervelocity, and bury the hidden danger of traffic hazard.In the traffic hazard of annual China, ninety percent is had to drive over the speed limit initiation.So how effectively monitoring overspeed of vehicle information and process in time, is reduce China's traffic hazard important measures.At present conventional method has Single-point velocity determination, test the speed in interval, flow and test the speed.Single-point velocity determination, when driver knows speed measuring point, can reduce the speed of a motor vehicle by brake and evade punishment, be easy to cause rear-end collision; Testing the speed in interval needs install multiple stage picture pick-up device in each interval and need traffic administration personnel to analyze, and can not process overspeed condition in real time; Flowing is tested the speed to install and is easily found and slow down to dodge onboard.The existing speed measuring device result that tests the speed is easily disturbed, and the result that tests the speed can only be used for punishing and not reach process overspeed condition in real time and avoid the object of the generation of accident.Existing apparatus and technology can not reach the purpose and meaning tested the speed, and cannot realize real-time monitoring, monitoring efficiency is low.Vehicular intelligent speed measuring device can realize Intelligent speed-measuring, and cost is low, and reliability is high, and Timeliness coverage excess speed event is reported to the police, and trouble-saving generation in advance, has huge economic benefit and social benefit.Also there is a lot of defect in the more existing Vehicular intelligent speed measuring devices of current China and technology, such as environmental adaptation study ability, and hypervelocity is failed to report, wrong report etc.
Summary of the invention
For solving the problem, the object of the present invention is to provide Vehicular intelligent speed measuring device and method.This device detection speed is fast, strong adaptability, can intelligent learning surrounding environment, and degree of intelligence is high, effectively can solve the situation reported by mistake and fail to report, and system detection results reliability is strong, and precision is high.
The technical solution used in the present invention is:
Vehicular intelligent speed measuring device and method comprise image capture module, memory module, display module, video processing module, alarm module, and detecting step is:
(1) image capture module gathers color video frequency image information, be transferred to video processing module and carry out vehicle speed measuring detection, be transferred to memory module, display module after compressed encoding simultaneously, memory module is put on record for file for storing real-time video information, and display module shows highway communication live state information in real time by after video decode.
(2) video processing module receives the color video frequency image information that image capture module transmission comes, test the speed that line, information of vehicles coupling upgrade by background modeling, foreground detection, setting, overspeed detection and alarming step, final detection obtains overspeed of vehicle information, and over-speed vehicles information transmission is shown over-speed vehicles to display module, the information transmission that simultaneously will exceed the speed limit is to alarm module, and over-speed vehicles information transmission is put on record, as punishing justification to memory module storage.
(3) alerting signal come of alarm module receiver, video processing module transmission, reports to the police, and reminds traffic administration personnel to exceed the speed limit in real time process, and checks over-speed vehicles and road conditions.
The Intelligent Recognition algorithm that Vehicular intelligent speed measuring device and method adopt comprises background modeling, line is tested the speed in foreground detection, setting, information of vehicles mate upgrade, overspeed detection and warning.
Described background modeling is exactly the process of this device intelligent learning ambient condition information, the video image information collected by video acquisition module, and setting up can the parameter that describes of quantitative and qualitative, makes system can have clear and definite information with reference to comparison.The image/video information of what the present invention collected is colored yuv format, by the Given information such as gray scale, color, saturation degree of image, sets up the information bank of self of each pixel.What the present invention adopted is that ADAPTIVE MIXED Gauss model carries out background modeling.The pixel of each correspondence of ADAPTIVE MIXED Gauss model sets up 3 ~ 5 Gauss models, the each pixel of the present invention sets up 4 Gauss models, and the Gauss model parameter describing each pixel is average gray, average chrominance, average staturation, model variance, Model Weight.First, set up first Gauss model of each pixel, transmit the value of Y, U, V information as the average gray in Gauss model parameter, average chrominance, average staturation of the pixel of the first two field picture come, weight value is 10, average gets 0, and its excess-three Gauss model average gray, average chrominance, average staturation get 0, and weight value is 1, average gets 0, and four equal values of Gauss model variance are 500; Transmit the average gray of the second two field picture come, average chrominance, average staturation and first Gauss model comparison, corresponding average gray, average chrominance, average staturation difference are less than certain threshold value, then first Gauss model weight adds 1, otherwise, stored in second Gauss model.In like manner, successively Gauss's training study is carried out to the image that transmission comes, if the Gauss model information at a new two field picture corresponding pixel points place is not all mated with existing four Gauss models, then replace the minimum Gauss model of weight and proceed study.When pixel has Gauss model weight to reach 300, then this pixel modeling success, modeling in image is successfully put and counts, if reach 1/3 of pixel sum in image, then background modeling success, each pixel modeling successful context parameter information is the information of the maximum Gauss model of weight.Meanwhile, background will proceed study, with scene change such as the light in conforming.
The weights of Gauss model can not infinitely increase, and when modeling point Gauss model weight limit reaches after 500, will reduce according to given pace, weights rule change is:
Wherein,
learning rate,
size relevant with context update speed,
larger, context update speed is faster,
less, context update speed is slower,
for the weight after a new two field picture Background learning,
for previous frame image Gauss model weight.
The update strategy of variance is:
Wherein,
for the model variance after a new two field picture Background learning,
for previous frame image Gauss model variance,
,
,
be respectively average gray, average chrominance, the average staturation of a new two field picture,
,
,
be respectively the average gray of current background, average chrominance, average staturation.
Described foreground detection is exactly the object of the new motion detected in present background.When after background modeling success, the new two field picture that transmission comes, while carrying out Background learning, also will carry out prospect modeling.Prospect modeling sets up Gauss model to each pixel equally, and the Gauss model of the Gauss model of foundation with background pixel point corresponding in background model is compared, if average gray, average chrominance, average staturation difference is less than certain threshold value, then current point is background dot, otherwise be doubtful foreground point, and then, the eight neighborhood of corresponding with background model for current pixel point Gauss model background pixel point is compared further, if average gray, average chrominance, average staturation difference is less than certain threshold value, then current point is background dot, otherwise be foreground point, more mainly swing to get rid of leaf etc. the interference caused with eight neighborhood pixel.After having traveled through all pixels, carry out expansion process to the foreground point filtered out, the pixel by same entity prospect carries out UNICOM.And then, calculate the girth of each UNICOM's prospect, area, center, depth-width ratio, screen out the prospect that area is too small.Meanwhile, whether be that vehicle judges in advance to prospect.Vehicle speed detector device is just sailed to vehicle the direction come and is installed, and highway foundation highway sideline information in camera fields of view, there is the difference of the depth of field, namely the position highway spacing that distance camera is near is wide and vehicle foreground information area is larger, away from camera position highway spacing is narrow and vehicle foreground information area is less, and then image is divided into three regions, in plane of delineation short transverse, on average be divided into 3 sections, and then the area detecting vehicle is maximum, minimum threshold information to arrange to respectively each section.And then, according to foreground depth of field information and prospect depth-width ratio, eliminating is not other foreground informations of vehicle, and marks different masks to different vehicle prospect respectively, and by the prospect information of vehicles in the first two field picture after background modeling success stored in foreground information storehouse.
Described setting is tested the speed the setting of the section that tests the speed that line refers to, and is namely set in a certain fixing section and completes the work of testing the speed.Assignment procedure is arrange two transverse directions and to stumble line sailing the direction come along vehicle, and spacing is too little in order to avoid test the speed inaccurate.Set speed of a motor vehicle alarm threshold value, the speed of a motor vehicle is reported to the police after exceeding threshold value simultaneously, assists traffic administration personnel to carry out hypervelocity process in time.
Described information of vehicles coupling upgrade refer to by foreground detection to information of vehicles go coupling renewal prospect historical information in time.Compared by the foreground information in the prospect information of vehicles that detects in a new two field picture and foreground information storehouse, compare according to prospect the center displacement, area, when meeting the center displacement and be less than threshold value and foreground area change being less than changed factor, be judged as same prospect vehicle, and new information of vehicles is upgraded historical information, if there is no the historical information of coupling, be then judged to be new prospect and stored in history information library, meanwhile, non-existent prospect is cleared up in history information library.
Described overspeed detection and warning, refer to when detect prospect vehicle center first time enter test the speed region time, zone bit bgetin assignment is true, and carry out Clocked operation, follow-up continuation is followed the tracks of foreground information, when the prospect vehicle of detecting roll away from test the speed region time, zone bit bgetout assignment is true, now timing stops, now, according to distance and the tracking time in the region of setting, just Vehicle Speed can be calculated, if hypervelocity, then report to the police, and recording information of vehicles.
The invention has the beneficial effects as follows: this device and detection method can assist transport personnel carry out overspeed detection and process in time, the generation of Accident prevention, be conducive to safeguarding traffic order and ensureing traffic safety, simultaneously, each traffic administration person can monitor multiple traffic section simultaneously, Intelligent Measurement, without omission, saves human cost.This installation cost is low, installs simple, easy to operate.Overspeed detection algorithm has higher intelligent and robustness, and reliability is high.
Accompanying drawing explanation
Accompanying drawing is used to provide a further understanding of the present invention, and forms a part for instructions, is used from explanation the present invention, is not construed as limiting the invention with example one of the present invention.In the accompanying drawings:
Fig. 1 is structured flowchart of the present invention.
Fig. 2 is Vehicular intelligent Computational Method of Velocity Measurement process flow diagram of the present invention.
Fig. 3 is overspeed of vehicle alarm condition key diagram of the present invention.
Embodiment
Below in conjunction with instantiation, each detailed problem involved by technical solution of the present invention is described.Be to be noted that described example is only intended to be convenient to the understanding of the present invention, therefore do not limit protection scope of the present invention.
As shown in Figure 1, above-mentioned Vehicular intelligent speed measuring device and method step is utilized to be:
(1) image capture module 1 gathers color video frequency image information, be transferred to video processing module 2 and carry out vehicle speed measuring detection, be transferred to memory module 5, display module 3 after compressed encoding simultaneously, memory module 5 is put on record for file for storing real-time video information, and display module 3 shows highway communication live state information in real time by after video decode.
(2) video processing module 2 receives the color video frequency image information that image capture module 1 transmission comes, test the speed that line, information of vehicles coupling upgrade by background modeling, foreground detection, setting, overspeed detection and alarming step, final detection obtains overspeed of vehicle information, and over-speed vehicles information transmission is shown over-speed vehicles to display module 3, the information transmission that simultaneously will exceed the speed limit is to alarm module 4, simultaneously by over-speed vehicles information transmission to memory module 5, as punishing justification.
(3) alerting signal come of alarm module 4 receiver, video processing module 2 transmission, reports to the police, and reminds traffic administration personnel to exceed the speed limit in real time process, and checks over-speed vehicles and road conditions.
As shown in Figure 2, the Intelligent Recognition algorithm that Vehicular intelligent speed measuring device and method adopt comprises background modeling, line is tested the speed in foreground detection, setting, information of vehicles mate upgrade, overspeed detection and warning.
Described background modeling is exactly the process of this device intelligent learning ambient condition information, the video image information collected by video acquisition module 1, and setting up can the parameter that describes of quantitative and qualitative, makes system can have clear and definite information with reference to comparison.The image/video information of what the present invention collected is colored yuv format, by the Given information such as gray scale, color, saturation degree of image, sets up the information bank of self of each pixel.What the present invention adopted is that ADAPTIVE MIXED Gauss model carries out background modeling.The pixel of each correspondence of ADAPTIVE MIXED Gauss model sets up 3 ~ 5 Gauss models, the each pixel of the present invention sets up 4 Gauss models, and the Gauss model parameter describing each pixel is average gray, average chrominance, average staturation, model variance, Model Weight.First, set up first Gauss model of each pixel, transmit the value of Y, U, V information as the average gray in Gauss model parameter, average chrominance, average staturation of the pixel of the first two field picture come, weight value is 10, average gets 0, and its excess-three Gauss model average gray, average chrominance, average staturation get 0, and weight value is 1, average gets 0, and four equal values of Gauss model variance are 500; Transmit the average gray of the second two field picture come, average chrominance, average staturation and first Gauss model comparison, corresponding average gray, average chrominance, average staturation difference are less than certain threshold value, then first Gauss model weight adds 1, otherwise, stored in second Gauss model.In like manner, successively Gauss's training study is carried out to the image that transmission comes, if the Gauss model information at a new two field picture corresponding pixel points place is not all mated with existing four Gauss models, then replace the minimum Gauss model of weight and proceed study.When pixel has Gauss model weight to reach 300, then this pixel modeling success, modeling in image is successfully put and counts, if reach 1/3 of pixel sum in image, then background modeling success, each pixel modeling successful context parameter information is the information of the maximum Gauss model of weight.Meanwhile, background will proceed study, with scene change such as the light in conforming.
The weights of Gauss model can not infinitely increase, and when modeling point Gauss model weight limit reaches after 500, will reduce according to given pace, weights rule change is:
Wherein,
learning rate,
size relevant with context update speed,
larger, context update speed is faster,
less, context update speed is slower,
for the weight after a new two field picture Background learning,
for previous frame image Gauss model weight.
The update strategy of variance is:
Wherein,
for the model variance after a new two field picture Background learning,
for previous frame image Gauss model variance,
,
,
be respectively average gray, average chrominance, the average staturation of a new two field picture,
,
,
be respectively the average gray of current background, average chrominance, average staturation.
Described foreground detection is exactly the object of the new motion detected in present background.When after background modeling success, the new two field picture that transmission comes, while carrying out Background learning, also will carry out prospect modeling.Prospect modeling sets up Gauss model to each pixel equally, and the Gauss model of the Gauss model of foundation with background pixel point corresponding in background model is compared, if average gray, average chrominance, average staturation difference is less than certain threshold value, then current point is background dot, otherwise be doubtful foreground point, and then, the eight neighborhood of corresponding with background model for current pixel point Gauss model background pixel point is compared further, if average gray, average chrominance, average staturation difference is less than certain threshold value, then current point is background dot, otherwise be foreground point, more mainly swing to get rid of leaf etc. the interference caused with eight neighborhood pixel.After having traveled through all pixels, carry out expansion process to the foreground point filtered out, the pixel by same entity prospect carries out UNICOM.And then, calculate the girth of each UNICOM's prospect, area, center, depth-width ratio, screen out the prospect that area is too small.Meanwhile, whether be that vehicle judges in advance to prospect.Vehicle speed detector device is just sailed to vehicle the direction come and is installed, and highway foundation highway sideline information in camera fields of view, there is the difference of the depth of field, namely the position highway spacing that distance camera is near is wide and vehicle foreground information area is larger, away from camera position highway spacing is narrow and vehicle foreground information area is less, and then image is divided into three regions, in plane of delineation short transverse, on average be divided into 3 sections, and then vehicle detection area is maximum, minimum threshold information to arrange to respectively each section.And then according to foreground depth of field information and prospect depth-width ratio, eliminating is not other foreground informations of vehicle, and marks different masks to different vehicle prospect respectively, and by the prospect information of vehicles of the first two field picture after background constructing success stored in foreground information storehouse.
Described setting is tested the speed the setting of the section that tests the speed that line refers to, and is namely set in a certain fixing section and completes the work of testing the speed.Assignment procedure is arrange two transverse directions and to stumble line sailing the direction come along vehicle, and spacing is too little in order to avoid test the speed inaccurate.Set speed of a motor vehicle alarm threshold value, the speed of a motor vehicle is reported to the police after exceeding threshold value simultaneously, assists traffic administration personnel to process in time.
Described information of vehicles coupling be updated to by foreground detection to information of vehicles go coupling renewal prospect historical information in time.Compared by the foreground information in the prospect information of vehicles that detects in a new two field picture and foreground information storehouse, compare according to prospect the center displacement, area, when meeting the center displacement and be less than threshold value and foreground area change being less than changed factor, be judged as same prospect vehicle, and new information of vehicles is upgraded historical information, if there is no the historical information of coupling, be then judged to be new prospect and stored in history information library, meanwhile, non-existent prospect is cleared up in history information library.
Described overspeed detection and warning, refer to when detect prospect vehicle center first time enter test the speed region time, zone bit bgetin assignment is true, and carry out Clocked operation, follow-up continuation is followed the tracks of foreground information, when the prospect vehicle of detecting roll away from test the speed region time, zone bit bgetout assignment is true, now timing stops, now, according to distance and the tracking time in the region of setting, just Vehicle Speed can be calculated, if hypervelocity, then report to the police, and recording information of vehicles.
As shown in Figure 3, when vehicle center arrive arrange stumble line A time, start timing, vehicle continue travel, when vehicle center arrive set by stumble line B time, timer expiration, now, can the time of testing the speed being obtained, getting final product intelligent computation Vehicle Speed according to setting line length of an interval degree of stumbling, and then the information whether exceeded the speed limit.
Any those skilled in the art may utilize the technology contents of above-mentioned announcement to be changed or be modified as the Equivalent embodiments of equivalent variations.But everyly do not depart from technical solution of the present invention content, any simple modification, equivalent variations and the remodeling done above embodiment according to technical spirit of the present invention, still belong to the protection domain of technical solution of the present invention.
Claims (6)
1. Vehicular intelligent speed measuring device and method, is characterized in that: described Vehicular intelligent speed measuring device and method comprise image capture module, memory module, display module, video processing module, alarm module.
2. apparatus and method according to claim 1, is characterized in that: described Vehicular intelligent speed measuring device and method detecting step are:
(1) image capture module gathers color video frequency image information, be transferred to video processing module and carry out vehicle speed measuring detection, be transferred to memory module, display module after compressed encoding simultaneously, memory module is put on record for file for storing real-time video information, and display module shows highway communication live state information in real time by after video decode;
(2) video processing module receives the color video frequency image information that image capture module transmission comes, test the speed that line, information of vehicles coupling upgrade by background modeling, foreground detection, setting, overspeed detection and alarming step, final detection obtains overspeed of vehicle information, and over-speed vehicles information transmission is shown over-speed vehicles to display module, the information transmission that simultaneously will exceed the speed limit is to alarm module, and by over-speed vehicles information transmission to memory module, as punishing justification;
(3) alerting signal come of alarm module receiver, video processing module transmission, reports to the police, and reminds traffic administration personnel to exceed the speed limit in real time process, and checks over-speed vehicles and road conditions.
3. apparatus and method according to claim 1, it is characterized in that: described background modeling utilizes the image/video information of the colored yuv format collected, by the Given information such as gray scale, color, saturation degree of image, set up the information bank of self of each pixel, utilize the pixel of each correspondence of ADAPTIVE MIXED Gauss model to set up 3 ~ 5 Gauss models, the Gauss model parameter describing each pixel is average gray, average chrominance, average staturation, model variance, Model Weight; Modeling procedure is: first, set up first Gauss model of each pixel, transmit the value of Y, U, V information as the average gray in Gauss model parameter, average chrominance, average staturation of the pixel of the first two field picture come, weight value is 10, average gets 0, and its excess-three Gauss model average gray, average chrominance, average staturation get 0, and weight value is 1, average gets 0, and four equal values of Gauss model variance are 500; Transmit the average gray of the second two field picture come, average chrominance, average staturation and first Gauss model comparison, corresponding average gray, average chrominance, average staturation difference are less than certain threshold value, then first Gauss model weight adds 1, otherwise, stored in second Gauss model; In like manner, successively Gauss's training study is carried out to the image that transmission comes, if the Gauss model information at a new two field picture corresponding pixel points place is not all mated with existing four Gauss models, then replace the minimum Gauss model of weight and proceed study; When pixel has Gauss model weight to reach 300, then this pixel modeling success, successfully puts modeling in image and counts, if reach 1/3 of pixel sum in image, then and background modeling success; Each pixel modeling successful context parameter information is the information of the maximum Gauss model of weight, and meanwhile, background will proceed study, with scene change such as the light in conforming.
4. apparatus and method according to claim 1, is characterized in that: the right value update rule of described background modeling Gauss model is:
Gauss model weight can not infinitely increase, and when modeling point Gauss model weight limit reaches after 500, will carry out minimizing upgrade according to given pace, wherein,
learning rate,
size relevant with context update speed,
larger, context update speed is faster,
less, context update speed is slower,
for the weight after a new two field picture Background learning,
for previous frame image Gauss model weight;
The update strategy of the variance of described background modeling Gauss model is:
Wherein,
for the model variance after a new two field picture Background learning,
for previous frame image Gauss model variance,
,
,
be respectively average gray, average chrominance, the average staturation of a new two field picture,
,
,
be respectively the average gray of current background, average chrominance, average staturation.
5. apparatus and method according to claim 1, it is characterized in that: in described Vehicular intelligent speed measuring device and method, foreground detection is exactly the object of the new motion detected in present background, be whether that vehicle carries out judging to adopt area field depth principle in advance to prospect, described vehicle speed detector device is just sailed direction to vehicle and is installed, and highway foundation highway sideline information in camera fields of view, there is the difference of the depth of field, namely the position highway spacing that distance camera is near is wide and vehicle foreground information area is larger, away from camera position highway spacing is narrow and vehicle foreground information area is less, and then image is divided into three regions, in plane of delineation short transverse, on average be divided into 3 sections, and then give each section setting area maximum respectively, minimum threshold information, and then, namely the foreground information that eliminating is not vehicle is can be used as according to foreground depth of field information.
6. apparatus and method according to claim 1, it is characterized in that: overspeed detection and warning in described Vehicular intelligent speed measuring device and method, refer to when detect prospect vehicle center first time enter test the speed region time, zone bit bgetin assignment is true, and carry out Clocked operation, follow-up continuation is followed the tracks of foreground information, when the prospect vehicle of detecting roll away from test the speed region time, zone bit bgetout assignment is true, now timing stops, now, according to distance and the tracking time in the region of setting, just Vehicle Speed can be calculated, if hypervelocity, then report to the police, and recording information of vehicles.
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| CN111243331A (en) * | 2019-04-23 | 2020-06-05 | 绿桥(泰州)生态修复有限公司 | On-site information identification feedback method |
| CN113053136A (en) * | 2019-12-26 | 2021-06-29 | 上海晋沙智能科技有限公司 | Road intelligent security monitored control system |
| CN114245033A (en) * | 2021-11-03 | 2022-03-25 | 浙江大华技术股份有限公司 | Video synthesis method and device |
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| JP4631036B2 (en) * | 2007-10-29 | 2011-02-16 | 東日本旅客鉄道株式会社 | Passer-by behavior analysis device, passer-by behavior analysis method, and program thereof |
| CN202404757U (en) * | 2012-01-12 | 2012-08-29 | 石家庄汉邦科技有限公司 | Handheld portable green-channel vehicle detector |
| CN202759522U (en) * | 2012-08-30 | 2013-02-27 | 常州硕欣电子科技有限公司 | High-speed spherical camera |
| CN203657881U (en) * | 2013-12-15 | 2014-06-18 | 郑州新力光电技术有限公司 | Light supplement detection device for high-capacity tank |
| CN205140209U (en) * | 2015-09-18 | 2016-04-06 | 天津艾思科尔科技有限公司 | Be provided with detecting device of mounting |
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| CN111243331A (en) * | 2019-04-23 | 2020-06-05 | 绿桥(泰州)生态修复有限公司 | On-site information identification feedback method |
| CN113053136A (en) * | 2019-12-26 | 2021-06-29 | 上海晋沙智能科技有限公司 | Road intelligent security monitored control system |
| CN114245033A (en) * | 2021-11-03 | 2022-03-25 | 浙江大华技术股份有限公司 | Video synthesis method and device |
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Application publication date: 20150311 |