CN103839415B - Traffic flow based on pavement image feature identification and occupation rate information getting method - Google Patents
Traffic flow based on pavement image feature identification and occupation rate information getting method Download PDFInfo
- Publication number
- CN103839415B CN103839415B CN201410102168.3A CN201410102168A CN103839415B CN 103839415 B CN103839415 B CN 103839415B CN 201410102168 A CN201410102168 A CN 201410102168A CN 103839415 B CN103839415 B CN 103839415B
- Authority
- CN
- China
- Prior art keywords
- time
- road surface
- pavement
- vehicle
- road
- 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
Abstract
It is an object of the invention to provide a kind of traffic flow based on pavement of road characteristics of image identification and the information getting method of occupation rate, the method is to use low-light (level) DSP camera (SD or high definition) as collection, arithmetic unit, gather picture frame by frame, camera frame is located at upper pavement surface, Real-time Collection monitored area pavement of road changing features information, the image gathered is processed by the high speed processor utilizing camera to carry；For pavement of road characteristics of image, devise BP neural network recognization method to carry out Real time identification, by identifying that pavement of road characteristics of image is obtained the major parameters such as special bus flow, vehicle average speed and vehicle time occupation rate by occlusion, coverage condition.This method obtains vehicle traveling information in road for real-time, accurate, as traffic signalization, the input of traffic guidance control system, can reduce traffic entirety and be delayed, improve level of service.
Description
Technical field
The invention belongs to intelligent transportation system (ITS) technical field, be specifically related to the letter of a kind of traffic flow and occupation rate
Breath acquisition methods.
Background technology
Along with the expansion of China's urban construction scale, vehicle is increasing, brings the biggest pressure, warp to road traffic
Often there is traffic congestion to occur, can the most accurately obtain traffic information if had, such as vehicle flowrate, occupation rate, as traffic signal
Control, the input of traffic guidance control system, play the effect of traffic guidance, alleviate current traffic pressure.
The method of the information such as traditional acquisition Real-time Road vehicle flow, including using buried induction coil, microwave thunder
Reach and the technology such as video identification for vehicle all exists certain limitation.Ground induction coil detection magnetic flux change is used to have quite
High accuracy, but engineering construction difficulty in actual use, need to destroy road surface, relatively easily damage and safeguard non-
Often difficulty；Additionally, install inconvenience on the road surface of bridge, overpass etc.Use traditional Video Detection identification vehicle main
It is identification based on vehicle basic feature, is easily affected by weather, illumination, night lights, thus accuracy of detection when some
The lowest, false drop rate is high, it is difficult to use as relieved traffic control input parameter.During microwave radar detection slower-velocity target, precision is relatively
Low, and equipment price is higher.
Summary of the invention
It is an object of the invention to provide a kind of traffic flow based on pavement of road characteristics of image identification and occupation rate
Information getting method, in real time, accurately obtains vehicle traveling information in road, as traffic signalization, traffic guidance control
The input of system processed, can reduce traffic entirety and be delayed, improve level of service.
Technical scheme is as follows:
The present invention uses low-light (level) DSP camera (SD or high definition) as collection, arithmetic unit, gathers picture, phase frame by frame
Erecting, in upper pavement surface, Real-time Collection monitored area pavement of road changing features information, utilizes the high speed processing that camera carries
The image gathered is processed by device；For pavement of road characteristics of image, devise BP neural network recognization method and carry out in real time
Identify, by identifying that pavement of road characteristics of image is obtained special bus flow, vehicle average speed by occlusion, coverage condition
With major parameters such as vehicle time occupation rates.
The method to realize step as follows:
Step 1: use low-light (level) high definition or SD DSP camera above carriageway surfacing about 6.0--9.0 rice towards adopting in real time
Collection road surface characteristic picture；
Step 2: obtained the initial data of image by the DM648 processor of camera, after then carrying out image procossing,
To representing pavement strip feature contour binary map；
Step 3: step 2 is obtained the input representing pavement strip feature contour binary map as BP neural network recognization,
Using BP neural network recognization method to calculate, show that whether setting regions road surface characteristic is by occlusion, recursion judges current road
Whether face is with the presence of vehicle；
Step 4: by the vehicle number of setting regions in statistics a period of time, calculates time of vehicle operation occupation rate；Pass through
The counting vehicle time by setting regions, obtain the instantaneous travel speed of vehicle, or be calculated the average speed of a period of time.
4.1 remember to when having covering or have the result blocked from the results change without covering or block according to current road feature
The lower time will be T1, then road surface characteristic have cover or have the state blocked to last till recognition result is changed to without covering or blocking
Time to write down the now time be T2；
4.2 when once road surface characteristic from unobstructed or covered block or cover time, the flow Num of automobile is carried out
Add 1, and this car is at the holding time △ T=T2-T1 through this section section；
The △ T-phase of one minute each interior car is added and to obtain T (unit second) by 4.3, and divided by 60 seconds, the time of being calculated was occupied
Rate Keep:
Keep = T/60 。
4.4 when testing the speed, it is assumed that two target recognition region distances are S, is △ T, then car by the time difference of two target areas
Travel speed is
V=S/△T
Obtain vehicle flowrate Num, time occupancy Keep and Vehicle Speed per minute.
Normally, road surface characteristic (seeing Fig. 6) be usually be made up of the graticule in roadside, including separate graticule, guide arrow,
Deceleration square, Chinese, Arabic numerals etc., also include the pattern that deceleration strip, new-old pavement boundary texture, road surface aberration are formed
Deng.When specifically applying, as the graticule in table 1 is all made up of white straight line and arrow, relative to the asphalt surface of grey black,
These graticule profiles are apparent from, it is the most eye-catching to contrast.In this manner it is possible to utilize the contour feature clearly of pavement strip, carry out
The detection of vehicle.When there is no occlusion graticule when, the pavement strip of collected by camera be linearly with the combination shape of arrow,
When having automobile to block graticule when, the profile that camera extracts is usually the feature of automobile, and the arrow contour feature of graticule
Contrast is clearly.Identify by occlusion or covering, whether pavement strip is detected whether current lane has vehicle, have good
Detection results.
The TI processor of a high speed is had, when camera Real-time Collection road surface inside the low-light (level) DSP camera that the present invention uses
After picture, processor obtains the initial data of image, by gray processing, image enhaucament, seeks the contour feature of image the most again, for
Improving the speed extracting contour feature, present invention uses look-up table, look-up table is by two integers between 0 ~ 255
(pixel value range in gray-scale map is 0 ~ 255), the value being added and subtracting each other, if greater than 255 take 255, take 0 less than 0.?
This value is stored in the array of a prior application, defines the fast table that can search calculating computing.Try to achieve road surface characteristic picture
Profile after use average gray value method, the road surface characteristic picture profile diagram obtained is carried out binaryzation, obtains having representative meaning
The pavement strip contour feature binary picture of the road surface characteristic of justice.
This method have employed BP neural network recognization method, and this is a kind of multilayer feedforword net by Back Propagation Algorithm training
Network, it can store the mapping relations of linear processes between substantial amounts of input and output by study, wherein be not required to
The mathematical formulae of this mapping relations is described.Its learning rules are to use rapid decrease method, by the back propagation of error
Adjust the weights between input layer and hidden layer, hidden layer and output layer, finally calculate the square-error of network, reach to set
Minima i.e. train and terminate.Before identification, manual intervention is gathered automatically the figure that road surface characteristic is not covered by automobile or blocks
One radix of picture that sheet one radix, road surface characteristic are covered by automobile or block, includes daytime, evening, rainy day, fine day respectively
Etc. state.These pictures are carried out picture processing, gray processing, strengthens, seek the binary map of contour feature, be then trained, fixed
The minimum error of justice training weights and maximum frequency of training, draw between input layer and hidden layer, hidden layer and output layer
Weights, keep these weights, as identification computing weights below.When the road surface characteristic figure after binaryzation is through processing
After, transfer the weights above preserved and carry out computing, it is possible to draw the result of road surface characteristic, if having vehicle on current road surface
On.Whether having the result of vehicle according to road surface, add up in a period of time, the vehicle number through the region, road surface of setting is with average
Speed, in the region, road surface of setting, vehicle is through the time occupancy of out-of-date vehicle.
Advantages of the present invention is as follows:
The present invention has the advantage that the interference source to conventional video detection method effectively filters, and both can be used alone and has obtained
Good Detection results, it is possible to traditional virtual coil or video detecting method multiplexing based on model, in any meteorology
Under the conditions of high lifting Video Detection accuracy.
This method have need not destroy pavement of road, low cost, easy to maintenance, can real-time video monitoring, detection accurate
Rate advantages of higher.Additionally, this product up-gradation is easy, gathering algorithm in use can be continued to optimize.
This method can be used for the crossing such as city, high speed, road section traffic volume data acquisition, particularly can solve long-standing problem and hand over
The flimsy shortcoming of messenger control coil, builds significant for smooth city, smart city.
Accompanying drawing explanation
Fig. 1 system and device operating diagram；
The overview flow chart of Fig. 2 present invention；
Fig. 3 image procossing flow graph；
The training flow process of Fig. 4 BP network；
The identification process of Fig. 5 BP network；
Fig. 6 pavement of road feature.
Detailed description of the invention
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention do not limit
In this.
See Fig. 1, implement the work signal of the present invention for certain crossing, in figure as a example by 3 tracks of standard, by low-light (level)
DSP camera sets up above pavement of road on about 6.5 meters high cross bars, inputs 12v power supply, is connected the power supply at crossing by netting twine
Ethernet switch in switch board, Ethernet switch can connect the optical fiber of wire transmission, or 3G wireless router.
By embedded in the real-time traffic flow amount that the camera that the present invention relates to algorithm obtains, time occupancy and vehicle mean velocity information first
By network cable transmission to Ethernet switch and target machine.According to practical situation, wired fiber-optic transfer, or 3G can be selected
It is wirelessly transferred.Additionally, camera I/O mouth data, RS485 data can by the corresponding interface protocol transmission to signal controlling machine or other should
Use equipment.
At night, or ray relative ratio on daytime is time dark, and low-light (level) DSP phase chance as required, is opened light compensating lamp, given
Road surface light filling, improves the accuracy rate identifying road surface characteristic with this, and smart camera can control the opening and closing of light compensating lamp.
In conjunction with the main-process stream of the inventive method that Fig. 2 shows, when certain track, low-light (level) DSP camera Real-time Collection picture,
Then dsp processor proceeds by image gray processing, image enhaucament, and image quickly seeks profile, and profile diagram carries out binaryzation, will
Binary picture is delivered to BP neutral net and is identified, and draws it is currently whether road surface identification is blocked by automobile or cover, and recursion is sentenced
Whether determine current road with the presence of vehicle.The vehicle number passed through in statistics a period of time, calculates time of vehicle operation occupation rate；Logical
Cross and calculate the time that vehicle passes through, obtain the instantaneous travel speed of vehicle, or be calculated the average speed of a period of time.
It is embodied as step as follows:
Step 1: use low-light (level) high definition or SD DSP camera about 6.0--9.0 rice, to adopt in real time above carriageway surfacing
Collection road surface characteristic picture.
Step 2: after the picture of camera Real-time Collection road surface, DM648 processor obtains the initial data of image, then
After carrying out image procossing, as the input of BP neural network recognization.Handling process such as Fig. 3:
First of all for the operation time of minimizing image, transfer the cromogram of 3 passages to single pass gray-scale map.Image enhaucament
It is able to more highlight the profile of pavement strip in picture, facilitates the segmentation in later stage.Seek the contour feature of image, in order to carry
The high speed extracting contour feature, employs look-up table in program, look-up table is by two integers between 0 ~ 255, phase
The value added and subtract each other, if greater than 255 take 255, take 0 less than 0.This value is stored in the array of a prior application,
Define the fast table that can search calculating computing.After trying to achieve road surface characteristic profile, the pixel average of statistics gray level image, make
Threshold value for binaryzation.Use this threshold value that profile diagram is carried out binaryzation, obtain representing the binary map of road surface characteristic.
Step 3: use BP neural network recognization method to calculate, show that whether setting regions road surface characteristic is by occlusion,
Recursion judges that whether current road is with the presence of vehicle.Process is as follows:
3.1 extract training sample, are identified the weights of road surface characteristic.Sample is divided into positive sample and negative sample, positive sample
Being the road surface characteristic figure not having occlusion, negative sample is the road surface characteristic figure having automobile to block, and sample is manually to gather, sample
Image size is 80x40 (wide 80 pixels, high 40 pixels), number several, the sample size of collection is the biggest, and the effect of identification is more
Good.The positive and negative samples figure collected, peace, according to the image processing flow of step 2, obtains the binary map of road surface characteristic.
The training process of 3.2 BP networks.Such as Fig. 4.
The input of training is the binary map of road surface characteristic obtained in the previous step.It is one group of weights after having trained, is saved in
In txt document.
The identification process of 3.3 BP networks.
The binary map of the road surface characteristic that 3.1 steps are obtained, by carrying out computing with weights obtained in the previous step, passes to hidden
Hiding layer, hidden layer passes to output layer with weights computing again, finally according to output layer, calculates, judges the result of output.Process such as figure
5。
Step 4: the vehicle number passed through in statistics a period of time, calculates time of vehicle operation occupation rate；By counting vehicle
The time passed through, obtain vehicle section travel speed, or be calculated the average speed of a period of time.
Write down to when having covering or have the result blocked from the results change without covering or block according to current road feature
Time is T1, then road surface characteristic have cover or have the state blocked last till recognition result be changed to without cover or block time
It is T2 that time writes down the now time.When once identify graticule from unobstructed or covered block or cover time, the flow of automobile
Num adds 1, and this car is at the holding time △ T=T2-T1 through this section section.By one minute interior each
The △ T-phase of car adds and obtains T (unit second), divided by 60 seconds of one minute, just calculates time occupancy Keep as follows:
Keep = T/60 。
When testing the speed, it is assumed that two target recognition region distances are S, it is △ T, then vehicle row by the time difference of two target areas
Sailing speed is
V=S/△T
Thus obtain vehicle flowrate Num, time occupancy Keep and Vehicle Speed per minute.Acquired results
Can send according to wired or radio transmitting method above, control as traffic lights and the foundation of urban traffic guidance.
The method that this invention relates to can detect target road surface with other video vehicle detection method multiplexing, such as this method
In the presence of feature, can be used as the Rule of judgment passed through without vehicle, particularly use the method at high light, shadow interference time serious
Video Detection capacity of resisting disturbance can be effectively improved.
The invention is not limited in above-mentioned embodiment, if various changes or deformation to invention are without departing from the present invention's
Spirit and scope, if within the scope of these are changed and deform claim and the equivalent technologies belonging to the present invention, then the present invention
It is also intended to comprise these change and deformation.
Claims (3)
1. traffic flow based on pavement of road feature identification and an information getting method for occupation rate,
Step 1: use low-light (level) high definition or SD DSP camera 6.0--9.0 rice Real-time Collection road surface characteristic above carriageway surfacing
Picture；
Step 2: obtained the initial data of image by the processor of camera, after then carrying out image procossing, obtain representing road surface
The binary map of feature；
Step 3: step 2 obtains the binary map the representing road surface characteristic input as BP neural network recognization, uses BP neural
Network Recognition method calculates, and show that whether setting regions road surface characteristic is by occlusion, and recursion judges whether current road has car
Exist；
Step 4: by the vehicle number of setting regions in statistics a period of time, calculates time of vehicle operation occupation rate；By counting
The vehicle time by setting regions, obtain vehicle section travel speed, or be calculated the average speed of a period of time, specifically
Step is as follows:
4.1 according to current road feature from without covering or the results change blocked is to when writing down when having covering or have the result blocked
Between be T1, then road surface characteristic have covering or have the state blocked to last till recognition result is changed to without covering or when block
The now time of writing down is T2；
4.2 when once identify road surface characteristic from unobstructed or covered block or cover time, the flow Num of automobile is carried out
Add 1, and this car is at the holding time △ T=T2-T1 through this section section；
The △ T-phase of one minute each interior car is added and to obtain T by 4.3, and the unit of T is the second, and divided by 60 seconds, the time of being calculated was occupied
Rate Keep:
Keep = T/60 ；
4.4 when testing the speed, it is assumed that two target recognition region distances are S, is △ T, then vehicle row by the time difference of two target areas
Sailing speed is
V=S/△T
Obtain vehicle flowrate Num, time occupancy Keep and Vehicle Speed per minute；
Described step 2 detailed process is as follows:
First, transfer three-channel cromogram to single pass gray level image, carry out image enhaucament；
Then, the contour feature of image is quickly sought: use look-up table, by two integers between 0 ~ 255, be added and subtract each other
Value, if greater than 255 take 255, take 0 less than 0, this value be stored in the array of a prior application, form fast calculation
Computing can look-up table；
Finally, after trying to achieve road surface contour feature, the pixel average of statistics gray level image, as the threshold value of binaryzation, to profile
Figure carries out binaryzation, obtains representing the binary map of road surface characteristic.
Traffic flow based on pavement of road feature identification the most according to claim 1 and the acquisition of information side of occupation rate
Method, it is as follows that the BP neural network recognization method of described step 3 calculates process:
3.1 extract training sample, are identified the weights of road surface characteristic: sample is divided into positive sample and negative sample, positive sample to be not
Having the road surface characteristic figure of occlusion, negative sample is the road surface characteristic figure having automobile to block；Positive and negative samples is carried out at image
Reason, obtains the binary map of pavement strip contour feature；
The training of 3.2 BP networks: the input of training is the binary map of pavement strip contour feature obtained in the previous step, has trained
It is one group of weights after one-tenth, is saved in txt document；
The identification of 3.3 BP networks: the binary map of pavement strip contour feature by carrying out computing with weights, pass to hidden layer,
Hidden layer passes to output layer with weights computing again, finally according to output layer, calculates, judges the result of output.
Traffic flow based on pavement of road feature identification the most according to claim 1 and 2 and the acquisition of information of occupation rate
Method, the processor of described camera is DM648 processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410102168.3A CN103839415B (en) | 2014-03-19 | 2014-03-19 | Traffic flow based on pavement image feature identification and occupation rate information getting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410102168.3A CN103839415B (en) | 2014-03-19 | 2014-03-19 | Traffic flow based on pavement image feature identification and occupation rate information getting method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103839415A CN103839415A (en) | 2014-06-04 |
| CN103839415B true CN103839415B (en) | 2016-08-24 |
Family
ID=50802870
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410102168.3A Active CN103839415B (en) | 2014-03-19 | 2014-03-19 | Traffic flow based on pavement image feature identification and occupation rate information getting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103839415B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108615385B (en) * | 2016-12-13 | 2021-04-20 | 比亚迪股份有限公司 | Road condition assessment method and device, vehicle and cloud server |
| CN107085964B (en) * | 2017-05-10 | 2018-10-26 | 深圳市夏瑞科技有限公司 | Vehicular automatic driving system based on image enhancement |
| CN109285352A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of vehicle checking method based on pavement markers |
| CN110322037A (en) * | 2018-03-28 | 2019-10-11 | 普天信息技术有限公司 | Method for predicting and device based on inference pattern |
| CN108734959A (en) * | 2018-04-28 | 2018-11-02 | 扬州远铭光电有限公司 | A kind of embedded vision train flow analysis method and system |
| CN109508631A (en) * | 2018-09-12 | 2019-03-22 | 上海电科智能系统股份有限公司 | A kind of traffic flow model construction method based on Distributed Optical Fiber Sensing Techniques |
| WO2020093351A1 (en) * | 2018-11-09 | 2020-05-14 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for identifying a road feature |
| CN109410578B (en) * | 2018-11-09 | 2021-01-12 | 浙江浩腾电子科技股份有限公司 | Traffic intersection section traffic flow detection method based on computer vision |
| CN109596092A (en) * | 2018-12-24 | 2019-04-09 | 上海智能交通有限公司 | A kind of usual friendship mouth sighting distance occlusion detection method and system based on vehicle-mounted vidicon |
| CN110310494A (en) * | 2019-05-21 | 2019-10-08 | 同济大学 | A kind of DETECTION OF TRAFFIC PARAMETERS method and system based on video image |
| CN113506264B (en) * | 2021-07-07 | 2023-08-29 | 北京航空航天大学 | Road vehicle number identification method and device |
| CN114821109A (en) * | 2022-05-10 | 2022-07-29 | 广东鉴面智能科技有限公司 | Image recognition system based on low-light-level visual algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0962979A (en) * | 1995-08-25 | 1997-03-07 | Toshiba Corp | Traffic flow predicting device |
| WO2011126215A2 (en) * | 2010-04-09 | 2011-10-13 | 고려대학교 산학협력단 | Traffic flow control and dynamic path providing system linked with real-time traffic network structure control based on bidirectional communication function-combined vehicle navigation, and method thereof |
| CN102542799A (en) * | 2011-12-19 | 2012-07-04 | 中山大学 | Line acquisition video vehicle detector based on pavement marker and detecting method thereof |
| CN102610103A (en) * | 2012-01-13 | 2012-07-25 | 深圳市黄河数字技术有限公司 | Intelligent traffic line analysis and recognition method |
| CN102789686A (en) * | 2012-07-10 | 2012-11-21 | 华南理工大学 | Road traffic flow detecting method based on road surface brightness composite mode recognition |
| CN103366578A (en) * | 2013-06-27 | 2013-10-23 | 北京文通图像识别技术研究中心有限公司 | Image-based vehicle detection method |
-
2014
- 2014-03-19 CN CN201410102168.3A patent/CN103839415B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0962979A (en) * | 1995-08-25 | 1997-03-07 | Toshiba Corp | Traffic flow predicting device |
| WO2011126215A2 (en) * | 2010-04-09 | 2011-10-13 | 고려대학교 산학협력단 | Traffic flow control and dynamic path providing system linked with real-time traffic network structure control based on bidirectional communication function-combined vehicle navigation, and method thereof |
| CN102542799A (en) * | 2011-12-19 | 2012-07-04 | 中山大学 | Line acquisition video vehicle detector based on pavement marker and detecting method thereof |
| CN102610103A (en) * | 2012-01-13 | 2012-07-25 | 深圳市黄河数字技术有限公司 | Intelligent traffic line analysis and recognition method |
| CN102789686A (en) * | 2012-07-10 | 2012-11-21 | 华南理工大学 | Road traffic flow detecting method based on road surface brightness composite mode recognition |
| CN103366578A (en) * | 2013-06-27 | 2013-10-23 | 北京文通图像识别技术研究中心有限公司 | Image-based vehicle detection method |
Non-Patent Citations (1)
| Title |
|---|
| 一种基于彩色路面区域分割的候选车辆区域视频检测方法;陈兆学 等;《计算机应用与软件》;20071031;第24卷(第10期);第31-36页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103839415A (en) | 2014-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103839415B (en) | Traffic flow based on pavement image feature identification and occupation rate information getting method | |
| CN103985250B (en) | The holographic road traffic state vision inspection apparatus of lightweight | |
| CN102819952B (en) | Method for detecting illegal lane change of vehicle based on video detection technique | |
| CN103077617B (en) | Based on crossing intelligent traffic light supervisory systems and the method for computer vision | |
| Tahmid et al. | Density based smart traffic control system using canny edge detection algorithm for congregating traffic information | |
| CN105336169B (en) | A kind of method and system that traffic congestion is judged based on video | |
| CN103714363B (en) | A kind of motor vehicle exhaust smoke video identification system | |
| CN100538763C (en) | Mixed traffic flow parameters detection method based on video | |
| CN101807352B (en) | Method for detecting parking stalls on basis of fuzzy pattern recognition | |
| CN106355903B (en) | Multilane traffic volume detection method based on video analysis | |
| CN103150903B (en) | Video vehicle detection method for adaptive learning | |
| CN101458871B (en) | Intelligent traffic analysis system and application system thereof | |
| CN107301776A (en) | Track road conditions processing and dissemination method based on video detection technology | |
| CN102385803B (en) | All-weather urban vehicle tracking and counting method based on video monitoring | |
| CN109191830A (en) | A kind of congestion in road detection method based on video image processing | |
| CN106128121B (en) | Vehicle queue length fast algorithm of detecting based on Local Features Analysis | |
| CN104599502A (en) | Method for traffic flow statistics based on video monitoring | |
| Pan et al. | Traffic surveillance system for vehicle flow detection | |
| CN100504318C (en) | Switching method between videos for detecting vehicles during day-night | |
| CN103295403B (en) | A kind of traffic flow visual inspection method | |
| CN102663357A (en) | Color characteristic-based detection algorithm for stall at parking lot | |
| CN103383733A (en) | Lane video detection method based on half-machine study | |
| CN102005120A (en) | Traffic intersection monitoring technology and system based on video image analysis | |
| CN105654720B (en) | Loop detector layout method based on urban road congestion identification | |
| CN102136195B (en) | Method for detecting road traffic condition based on image texture |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Dai Gao Inventor after: Wang Ran Inventor after: Cang Daodong Inventor before: Dai Gao Inventor before: Wang Ran Inventor before: Cang Daodong |
|
| COR | Change of bibliographic data | ||
| CB02 | Change of applicant information |
Address after: 400060 Chongqing Nan'an District by the West Development Zone Nanping plant No. 5-2 Applicant after: CHONGQING ULIT TECHNOLOGY CO., LTD. Address before: 400060 Chongqing Nan'an District by the West Development Zone Nanping plant No. 5-2 Applicant before: CHONGQING ULIT TECHNOLOGY CO., LTD. |
|
| COR | Change of bibliographic data | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |