CN103325255A - Regional traffic condition detection method based on photogrammetric technology - Google Patents
Regional traffic condition detection method based on photogrammetric technology Download PDFInfo
- Publication number
- CN103325255A CN103325255A CN2013102690454A CN201310269045A CN103325255A CN 103325255 A CN103325255 A CN 103325255A CN 2013102690454 A CN2013102690454 A CN 2013102690454A CN 201310269045 A CN201310269045 A CN 201310269045A CN 103325255 A CN103325255 A CN 103325255A
- Authority
- CN
- China
- Prior art keywords
- traffic
- key element
- transportation
- photogrammetric
- hidden trouble
- 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.)
- Granted
Links
Images
Abstract
The invention discloses a regional traffic condition detection method based on the photogrammetric technology. The regional traffic condition detection method based on the photogrammetric technology comprises the following steps of laying out equipment, obtaining traffic factors, obtaining the motion features of the traffic factors, judging the traffic status and the like. The regional traffic condition detection method based on the photogrammetric technology is free of influence of the natural environment, low in false alarm rate, wide in detection range, and capable of achieving abundant functions.
Description
Technical field
The invention belongs to regional traffic condition monitoring field, particularly a kind of method of carrying out the regional traffic condition detection based on photogrammetric technology.
Background technology
Many scientific ﹠ technical corporation of home and abroad have all released the video intelligent traffic product of oneself at present.For example: the VD video vehicle detection system ' of Belgian TrafCon company research and development, " epoch No. one ", the VTD3000 system of Shenzhen Hagongda Traffic Electronic Technology Co., Ltd. of Beijing's Astronavigation Age Science and Technology Ltd., said system can realize that traffic data collection, traffic events detect and intersection signal control optimization etc.
Its core technology of these systems is to use the variation of video, utilizes prospect and background segment check-up traffic events, is subjected to the shortcomings such as natural environment influence is large, rate of false alarm is high, examination scope is limited but exist, and effect is unsatisfactory in actual applications.
Photogrammetricly refer to by the obtaining of image research target subject spatial information, process, an information science of extraction and result provision.Photogrammetry is the subdiscipline of mapping science, and its main task is be used to the topomap of surveying and drawing various engineer's scales, sets up digital terrain model (DTM), for various Geographic Information System and land information system provide basic data.
The subject matter that photogrammetry solves is geometry location, namely determines size, shape and the locus of subject.The ultimate principle of geometry location comes from the forward intersection method of surveying, and it is according to two known photography websites and two known photography direction lines, and intersection goes out to consist of the three-dimensional coordinate of the point to be located of these two photography light.
The difference of the residing position of video camera during according to photography, photogrammetry can be divided into terrestrial photogrammetry, photogrammetric measurement and space photogrammetry.According to the difference of application, photogrammetry can be divided into again field observation and measure and non-topographic photogrammetry two large classes.According to the difference (also being the difference of historical stage) of technical finesse means, photogrammetry can be divided into analog photogrammetry, analytical photogrammetry and digital photogrammetry again.
Summary of the invention
The object of the present invention is to provide a kind of photogrammetric technology of utilizing that the regional traffic situation is detected, be not subjected to natural environment influence, rate of false alarm is low, sensing range is large carries out the method for regional traffic condition detection based on photogrammetric technology.
The technical solution that realizes the object of the invention is:
A kind of method of carrying out the regional traffic condition detection based on photogrammetric technology may further comprise the steps:
Step 1: implantation of device: photography, picture pick-up device that at least two intervals are set;
Step 2: utilize photography, picture pick-up device to obtain the volume coordinate collection of all things in detected zone according to photogrammetric survey method, obtain a cloud, to put cloud according to dense degree, distribution situation and the coordinate information of a cloud and be divided into different traffic key elements, wherein, the traffic key element comprises barrier, the vehicles, means of transportation and people, and identify the physical features of each traffic key element, its physical features comprises position, size, the quantity of traffic key element;
Step 3: continuous detecting is carried out in the zone, determine the motion feature of traffic key element in three dimensions according to the variation of the coordinate position of a cloud, its motion feature comprises direction, track and the trend of motion;
Step 4: to the judgement of traffic behavior:
(1) judges whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the physical features of the barrier that detects;
(2) judge whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the variation of the shape of means of transportation or position;
(3) vehicles and human motion feature and this regional traffic rules are compared, detect break in traffic rules and regulations;
(4) calculate the detection of the operating position of traffic resource such as road occupying rate, parking stall use, queue length, congestion in road situation according to the vehicles and people's position, quantity, relativeness.
The present invention compared with prior art, its remarkable advantage:
(1) the present invention utilizes photogrammetric technology with the digitizing of traffic key element, utilizes the digitizing means to improve traffic detection and management level.
(2) testing result of the present invention is not subjected to the impact of the physical environments such as shadow, can accurately distinguish shade, hot spot and vehicle, barrier, pedestrian etc., and the accuracy of judgement degree is higher.
(3) the present invention utilizes the measurement moment image to carry out the traffic behavior detection, and the accuracy in detection of moving object is high.
(4) the present invention can carry out the timesharing detection to a plurality of zones, and does not affect accuracy of detection, and plant factor is higher.
Below in conjunction with accompanying drawing the present invention is described in further detail.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method.
Embodiment
A kind of method of carrying out the regional traffic condition detection based on photogrammetric technology of the present invention may further comprise the steps:
Step 1: implantation of device: photography or camera head that at least two intervals are set;
Step 2: utilize photography, picture pick-up device to obtain the volume coordinate collection of all things in detected zone according to photogrammetric survey method, obtain a cloud, to put cloud according to dense degree, distribution situation and the coordinate information of a cloud and be divided into different traffic key elements, wherein, the traffic key element comprises barrier, the vehicles, means of transportation and people, and identify the physical features of each traffic key element, its physical features comprises position, size, the quantity of traffic key element;
Measuring method described here is prior art, and photogrammetric survey method is exactly the 3 D stereo coordinate that the pixel transitions on the image is become the space, and being integrated into of coordinate points is called a cloud in photogrammetric; The photogrammetric technology that relates generally to is camera calibration technology, the image that gathers tested zone, image is processed, the stereogram Auto-matching, determined the pixel space coordinate; Specific as follows:
Step 1: the demarcation of camera, namely according to the monumented point that in advance arranges and measure, and with the inside and outside parameter of this monumented point correcting camera;
Step 2: the image that gathers tested zone;
Step 3: image is processed; Utilize image processing algorithm that image is processed, the image processing algorithm that relates to mainly contains: gray scale correction, filtering and noise reduction, figure image intensifying, image segmentation, image binaryzation, image plus and minus calculation, feature point extraction scheduling algorithm;
Step 4: stereogram Auto-matching; The fast and accurately coupling that pixel is right, traffic zone pixel space coordinate extracts, and the pixel space coordinate is the basis of reconstruction region traffic;
Step 5: determine the pixel space coordinate, the volume coordinate collection is a cloud;
Step 3: continuous detecting is carried out in the zone, determine the motion feature of traffic key element in three dimensions according to the variation of the coordinate position of a cloud, its motion feature comprises direction, track and the trend of motion; Namely compare repeatedly check result, judge the difference situation of change of son point cloud, and then extrapolate motion and the situation of change of traffic key element;
Traffic key element motion feature calculates
The motion feature of the twice testing result calculating people in contrast front and back, car (comprise whether being kept in motion, and direction of motion, movement velocity etc.).Can use following several method but be not limited to make in the following method, utilize changes in coordinates to calculate motion feature.
1, obvious characteristic point method: the position coordinates of registration of vehicle unique point the most obvious, that the most easily identify, through repeatedly checking the situation of change of the volume coordinate of calculating this unique point.
2, minimum encirclement method: utilize minimum cube to wrap up all unique points, calculate cube centre coordinate situation of change.
3, average coordinates method: the volume coordinate of all unique points that can extract is averaged, calculate the situation of change of this mean value.
Utilize direction of motion and the distance of the change calculations object to be detected of the coordinate that above-mentioned each method calculates, utilize distance divided by calculating movement velocity detection time, when detected material was static, this centre coordinate changed all the time less than measuring error.
Step 4: to the judgement of traffic behavior:
(1) judges whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the physical features of the barrier that detects;
Road is shed quality testing and is surveyed: have in the zone to meet the key element of shedding the thing feature.The square barrier in the 20cm left and right sides occurs such as highway, size is high at 1.5m, long and wide pedestrian less than 0.6m.
(2) judge whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the variation of the shape of means of transportation or position;
The variation of means of transportation detects: detect the height change (can make increase, also can make drop to) of certain position of road when reaching certain limit, thinking that the larger potential safety hazard of impact appears in this road, may be surface doming, surface collapse, bridge collapse etc.
(3) vehicles and human motion feature and this regional traffic rules are compared, detect break in traffic rules and regulations;
(4) calculate the detection of the operating position of traffic resource such as road occupying rate, parking stall use, queue length, congestion in road situation according to the vehicles and people's position, quantity, relativeness.
Following distance detects: utilize (the seeing 3.3) such as obvious characteristic point methods, average coordinates method of minimum encirclement method, vehicle, the spacing between the calculating vehicle.
The parking stall is detected: the height change on parking stall, and uprise and reach certain numerical value, as calculate this regional height and increase more than the 18cm, think that this parking stall is occupied or use.
The method that obtains the traffic key element in the step 2 is: utilize the automatic structure realm digital terrain model of photogrammetric technology, utilization photography, picture pick-up device obtain the traffic key element according to the volume coordinate collection of all things in the detected zone of photogrammetric survey method acquisition and the difference of digital terrain model.
The method that obtains the traffic key element in the step 2 is: the volume coordinate collection relative difference that utilizes photography, picture pick-up device to obtain all things in detected zone according to photogrammetric survey method obtains the traffic key element.
The method that the variation of the shape of means of transportation or position is judged is for judging according to the variation of the shape of the means of transportation of the shape of means of transportation in the zone digit ground model of having set up or position and detection or position whether hidden trouble of traffic exists quantity and the degree with hidden trouble of traffic.
The method that the variation of the shape of means of transportation or position is judged is for judging according to the variation of means of transportation in the repeated detection whether hidden trouble of traffic exists quantity and the degree with hidden trouble of traffic.
Utilize the inventive method comprehensively to detect the regional traffic situation:
1, utilizes the traffic key element to judge and detect traffic
1) road is shed the quality testing survey: have in the zone to meet the key element of shedding the thing feature.The square barrier in the 20cm left and right sides occurs such as highway, size is high at 1.5m, long and wide pedestrian less than 0.6m
2) following distance detects: utilize (the seeing 3.3) such as obvious characteristic point methods, average coordinates method of minimum encirclement method, vehicle, the spacing between the calculating vehicle.
3) parking stall is detected: the height change on parking stall, and uprise and reach certain numerical value, as calculate this regional height and increase more than the 8cm, think that this parking stall is occupied or use.
4) the traffic things of other influences safety detects, detect the height change (can make increase, also can make drop to) of certain position of road when reaching certain limit, thinking that the larger potential safety hazard of impact appears in this road, may be uncertain thing, surface doming, surface collapse, the bridge collapse etc. shed.
5) vehicle is differentiated
2, traffic hazard is judged
The determination methods of traffic hazard mainly adopts following 4 kinds but is not limited to method in 4
1) the vehicle gap is too small, even when negative value occurring.
2) two groups or above dense feature point appear.
3) meet Accident Characteristic: detected length breadth ratio changes, and length breadth ratio does not meet existing vehicle classification.
Detecting vehicle stop may accident.
3, flow detection
1) quantity in the surveyed area: the quantity of the traffic key element that statistics is identified in certain spatial areas.
2) through certain regional quantity: the quantity of identifying the traffic key element of the identification that has motor behavior and pass through this zone.
4, judgement violating the regulations
1) pedestrian: the pedestrian appears in highway, and the pedestrian appears in car lane, occurs the pedestrian during red light on the road.
2) vehicle line ball: the edge feature point of vehicle is positioned at graticule coordinate top; The minimum of vehicle cube is surrounded by the part above the graticule coordinate, and the wheel arch unique point of vehicle is positioned at graticule coordinate top.
3) drive in the wrong direction to detect: by judging the direction of motion degree that conforms to this space communication rule, can check the situations such as retrograde, turning roadway craspedodrome, Through Lane turning.
4) stopping: vehicle is arranged in the zone, and remain static---variation of the obvious characteristic of this vehicle of repeated detection, minimum encirclement, coordinate average detects in the error range.
5, the means of transportation operating position is judged
1) occupancy rate detection method: the occupancy of how much calculating of utilizing the traffic element that detects in this space; Utilize the variation estimation occupancy of dispersed elevation; Utilize the vehicle gap to calculate occupancy, the plane projection in space deducts the summation in gap.
2) queue length detection method: the distance of the positions such as this coordinate of coordinate Calculation of detection queuing tail and crossing; Calculate the coordinate difference that team begins and finishes;
3) traffic congestion: utilize form speed to judge: the travel speed road that obviously descends gets congestion; Utilize occupancy to judge: occupancy raises and occurs blocking up; Utilize the vehicle gap to judge: the mean gap of vehicle diminishes, vehicle congestion.
4) parking stall is detected
Utilize contour feature, minimum encirclement, unique point average to judge whether that vehicle is parked on the parking stall, both the operating position of parking stall
Utilize the gap between vehicle, judged whether vacant parking stall, the lateral direction of car spacing thinks that greater than a parking stall distance parking stall is arranged.
The parking stall elevation occurs and changes, and other barriers are perhaps arranged, and thinks that the parking stall is occupied.
6, means of transportation detect
1) size changes: the situations such as barrier, road collapsion, bridge collapse that can detect road occur.
2) position changes: sign label damages or is artificial mobile, might cause traffic safety problem.
3) shape or position change: means of transportation or affiliated facility are damaged.
Utilize the automatic structure realm digital terrain model of photogrammetric technology, the characteristics of the method for the difference acquisition traffic key element of the volume coordinate collection of all things that utilization is photographed, picture pick-up device obtains detected zone according to photogrammetric survey method and digital terrain model are as follows:
1, sets up digital terrain model
Utilize photogrammetric detection data that surveyed area is carried out digitizing, make up the digital terrain model (DTM) that comprises road, bridge, tunnel and parking lot and affiliated facility thereof.These data can be used as follow-up identification traffic key element and judge the basic data of traffic behavior.
2, particular range detects
Only calculate the interior some cloud in zone that the user is concerned about, and only contrast the data in this regional spatial dimension, improve computing velocity.
3, automatic Calibration
1) utilize special sign to demarcate
Significantly sign easy to identify is installed in surveyed area, and measures in advance the coordinate of each sign.System identifies automatically according to the characteristics of sign, and according to the inside and outside parameter of coordinates correction camera of sign.
2) utilize provincial characteristics to demarcate
Utilize the fixed objects such as sign board on the road, graticule, bridge, street lamp as the inside and outside parameter that indicates the point calibration camera.
3) demarcate in advance
The inside and outside parameter of calibration for cameras is determined the inside and outside parameter that camera is current with the matching relationship of control camera head angle and focal length parameter.
4, many scenes
Utilize photography, the The Cloud Terrace of picture pick-up device and the zoom function of camera, allow photography, a plurality of scenes of picture pick-up device poll, increase sensing range, the raising plant factor.
5, known state is rejected
When surveyed area has the working-yard, the friendships such as the sign label of piling up, Anti-collision barrel are installed when executing.By arranging, the testing result in characteristics zone can be ignored in the specific time period by system, avoids repetition of alarms.
Embodiment 1:
Follow above-mentioned technical step, place barrier at certain road, barrier is separately positioned on apart from video camera 200M, 500M and 900M place, and the barrier of setting comprises, 195 tire, the carton of 30x30x20cm and 10x20x20cm.
Photography, the picture pick-up device of at least two interval 28.3m are set;
Make up the DTM of road, measure a transversal section, planimetric position and the elevation of 8 grid points of each cross-sectioning for per 15 meters among the embodiment.The three-dimensional coordinate of 8 grid points of one of them transversal section sees the following form:
| Grid points | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Horizontal ordinate | 0.225 | 3.705 | 7.586 | 11.466 | 15.553 | 19.410 | 23.268 | 26.716 |
| Ordinate | 8.984 | 8.697 | 10.087 | 11.478 | 10.057 | 8.093 | 6.126 | 6.274 |
| Elevation | 42.388 | 42.407 | 42.505 | 42.483 | 42.556 | 42.481 | 42.387 | 42.421 |
Inside and outside parameter behind the camera calibration comprises: the three-dimensional coordinate at photographic field lens center, the spatial attitude of photographic perpendicular, objective focal length and optical lens distortion coefficient.The s internal and external orientation of two photography and vedio recording equipment sees the following form:
| S internal and external orientation | Left photo | Right photo |
| Object lens x coordinate | 0.581 | 28.968 |
| Object lens y coordinate | 54.561 | 54.657 |
| Object lens z coordinate | 211.228 | 209.468 |
| The photo angle of pitch | 0.129909731373891 | -0.149957529672916 |
| The photo angle of roll | -0.0907850031756845 | -0.0869651113684297 |
| The photo rotation angle | -0.036281204021685 | 0.00336946979484145 |
| Objective focal length | 6494.98 | 6219.05 |
| Optical lens distortion coefficient | -8.2E-09 | -4.4E-09 |
The calculation level cloud, the poor acquired disturbance thing information of utilization point cloud and DTM:
All detect accurately tire, rectangle barrier at three position systems.
Tire height at 900m is 200mm, and the carton of 10cm detects the barrier for outstanding ground 90mm;
Tire height at the 500m place is 190mm, and the carton of 10cm detects the barrier for outstanding ground 95mm;
Tire height at the 200m place is 195mm, and the carton of 10cm detects the barrier for outstanding ground 95mm.
Especially there is identification plate at the 800m place at the shade on road surface, utilizes photogrammetric survey method, and it is 2mm that system can accurately detect the shade height, and for not affecting the environment shade of traffic, system is accurately rejected.
Utilize above-mentioned detection method, accurately cognitive disorders thing, shade.Utilize the method for cognitive disorders thing also can accurately identify the other influences traffic safety factors such as road swag, means of transportation change in location, highway pedestrian, visible the present invention can carry out widespread use in practice.
Embodiment 2:
Follow above-mentioned technical step, road with photography, the photography of picture pick-up device primary optical axis alignment distance, picture pick-up device 200m place, detect the retrograde situation of vehicle, because the probability that vehicle drives in the wrong direction seldom, traffic rules with the road both sides in system exchange, all vehicles are retrograde vehicle like this, because this set can cause a large amount of vehicles to drive in the wrong direction, and the retrograde vehicle automatic tracking function of system's cancellation.
10 minutes test durations, 13 of the oversize vehicles of process photography, picture pick-up device, 29 of minibuses.
The retrograde vehicle of native system report is 42.
By above-mentioned experiment as can be known this method to vehicle drive in the wrong direction, the situations such as vehicle line ball, turning roadway craspedodromes, Through Lane turnings, parking violation all have good behaviour, are containing that effectively the hidden trouble of traffic that causes owing to break in traffic rules and regulations is significant.
Embodiment 3:
There is traffic mark board at 814m place in the surveyed area, repeatedly 600～900m zone is detected, and the site error of the x of sign board coordinate, y, z is all less than 100mm, and physical dimension does not change, and system's form means of transportation are intact.Deducibility goes out when means of transportation change in location more than the 10cm or the change of shape more than the 5cm occur thus, system can change by automatic-prompting means of transportation state, may there be hidden trouble of traffic, for traffic administration person provides important information, for the traffic trip people provides traffic support significant.
Embodiment 4:
Photography, picture pick-up device primary optical axis are aimed at the 500m place of surveyed area, the mean value of the difference of some cloud coordinate and ground coordinate in the surveyed area, change to 50mm from 20mm, the high capacity waggon showed increased of road, the vehicle average velocity in vehicle detection zone has 103km/h to drop to 86km/h, the headstock distance is less than 200m, the prompting that system's generation road may occur blocking up, system prompt is consistent with the road driving truth, useful said method has good behaviour to the aspects such as parking stall management in traffic congestion, queue length, the parking lot.
Claims (5)
1. a method of carrying out the regional traffic condition detection based on photogrammetric technology is characterized in that, may further comprise the steps:
Step 1: implantation of device: photography or camera head that at least two intervals are set;
Step 2: utilize photography, picture pick-up device to obtain the volume coordinate collection of all things in detected zone according to photogrammetric survey method, obtain a cloud, to put cloud according to dense degree, distribution situation and the coordinate information of a cloud and be divided into different traffic key elements, wherein, the traffic key element comprises barrier, the vehicles, means of transportation and people, and identify the physical features of each traffic key element, its physical features comprises position, size, the quantity of traffic key element;
Step 3: continuous detecting is carried out in the zone, determine the motion feature of traffic key element in three dimensions according to the variation of the coordinate position of a cloud, its motion feature comprises direction, track and the trend of motion;
Step 4: to the judgement of traffic behavior:
(1) judges whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the physical features of the barrier that detects;
(2) judge whether to exist quantity and the degree of hidden trouble of traffic and hidden trouble of traffic according to the variation of the shape of means of transportation or position;
(3) vehicles and human motion feature and this regional traffic rules are compared, detect break in traffic rules and regulations;
(4) calculate the detection of the operating position of traffic resource such as road occupying rate, parking stall use, queue length, congestion in road situation according to the vehicles and people's position, quantity, relativeness.
2. a kind of method of carrying out the regional traffic condition detection based on photogrammetric technology according to claim 1, it is characterized in that, the method that obtains the traffic key element in the described step 2 is: utilize the automatic structure realm digital terrain model of photogrammetric technology, utilization photography, picture pick-up device obtain the traffic key element according to the volume coordinate collection of all things in the detected zone of photogrammetric survey method acquisition and the difference of digital terrain model.
3. a kind of method of carrying out the regional traffic condition detection based on photogrammetric technology according to claim 1, it is characterized in that, the method that obtains the traffic key element in the described step 2 is: the volume coordinate collection relative difference that utilizes photography, picture pick-up device to obtain all things in detected zone according to photogrammetric survey method obtains the traffic key element.
4. a kind of method of carrying out the regional traffic condition detection based on photogrammetric technology according to claim 2, it is characterized in that, described to means of transportation shape or the method judged of the variation of position for judging according to the variation of the shape of the means of transportation of the shape of means of transportation in the zone digit ground model of having set up or position and detection or position whether hidden trouble of traffic exists quantity and the degree with hidden trouble of traffic.
5. a kind of method of carrying out the regional traffic condition detection based on photogrammetric technology according to claim 3, it is characterized in that, described to means of transportation shape or the method judged of the variation of position for judging according to the variation of means of transportation in the repeated detection whether hidden trouble of traffic exists quantity and the degree with hidden trouble of traffic.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310269045.4A CN103325255B (en) | 2013-06-29 | 2013-06-29 | The method of region transportation situation detection is carried out based on photogrammetric technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310269045.4A CN103325255B (en) | 2013-06-29 | 2013-06-29 | The method of region transportation situation detection is carried out based on photogrammetric technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103325255A true CN103325255A (en) | 2013-09-25 |
| CN103325255B CN103325255B (en) | 2016-01-20 |
Family
ID=49193973
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310269045.4A Expired - Fee Related CN103325255B (en) | 2013-06-29 | 2013-06-29 | The method of region transportation situation detection is carried out based on photogrammetric technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103325255B (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107221175A (en) * | 2017-05-31 | 2017-09-29 | 深圳市鸿逸达科技有限公司 | A kind of pedestrian is intended to detection method and system |
| CN107423679A (en) * | 2017-05-31 | 2017-12-01 | 深圳市鸿逸达科技有限公司 | A kind of pedestrian is intended to detection method and system |
| CN107784844A (en) * | 2016-08-31 | 2018-03-09 | 百度在线网络技术（北京）有限公司 | Intelligent traffic lamp system and its road environment detection method |
| CN108182811A (en) * | 2018-01-04 | 2018-06-19 | 山东华夏高科信息股份有限公司 | A kind of road safety perils detecting system |
| CN108198419A (en) * | 2018-01-04 | 2018-06-22 | 山东华夏高科信息股份有限公司 | A kind of Intelligent road traffic safety hidden troubles removing system |
| CN108496176A (en) * | 2015-12-16 | 2018-09-04 | 法雷奥开关和传感器有限责任公司 | The method for identifying the object in the peripheral region of motor vehicles, driver assistance system and motor vehicles |
| CN109243181A (en) * | 2018-09-21 | 2019-01-18 | 深圳市轱辘汽车维修技术有限公司 | Traffic accident method for early warning, device, terminal device and storage medium |
| CN109345829A (en) * | 2018-10-29 | 2019-02-15 | 百度在线网络技术（北京）有限公司 | Monitoring method, device, equipment and the storage medium of unmanned vehicle |
| TWI651696B (en) * | 2017-12-29 | 2019-02-21 | 技嘉科技股份有限公司 | Systems and methods for monitoring traffic accident |
| CN109661667A (en) * | 2016-10-14 | 2019-04-19 | 富士通株式会社 | The retrograde detection device of vehicle and method, electronic equipment |
| CN109993976A (en) * | 2017-12-29 | 2019-07-09 | 技嘉科技股份有限公司 | Traffic accident monitors system and method |
| TWI674210B (en) * | 2017-12-04 | 2019-10-11 | 財團法人資訊工業策進會 | System and method for detecting dangerous vehicle |
| WO2020029013A1 (en) * | 2018-08-06 | 2020-02-13 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining traffic conditions |
| CN110998687A (en) * | 2017-08-08 | 2020-04-10 | 索尼公司 | Control system and control method |
| CN113299102A (en) * | 2021-03-27 | 2021-08-24 | 宁波工程学院 | Underground parking lot navigation method, system and storage medium |
| CN116453065A (en) * | 2023-06-16 | 2023-07-18 | 云途信息科技(杭州)有限公司 | Road surface foreign matter throwing identification method and device, computer equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100296705A1 (en) * | 2007-11-07 | 2010-11-25 | Krysztof Miksa | Method of and arrangement for mapping range sensor data on image sensor data |
| CN202057328U (en) * | 2011-04-12 | 2011-11-30 | 清华大学 | Vehicle-mounted scale-free traffic accident scene quick surveying system based on binocular vision |
| CN102445186A (en) * | 2011-09-28 | 2012-05-09 | 中交第二公路勘察设计研究院有限公司 | Method for generating road design surface information by laser radar scan |
| CN102592454A (en) * | 2012-02-29 | 2012-07-18 | 北京航空航天大学 | Intersection vehicle movement parameter measuring method based on detection of vehicle side face and road intersection line |
| CN102661736A (en) * | 2012-05-17 | 2012-09-12 | 天津市星际空间地理信息工程有限公司 | Highway reorganization and expansion surveying method |
-
2013
- 2013-06-29 CN CN201310269045.4A patent/CN103325255B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100296705A1 (en) * | 2007-11-07 | 2010-11-25 | Krysztof Miksa | Method of and arrangement for mapping range sensor data on image sensor data |
| CN202057328U (en) * | 2011-04-12 | 2011-11-30 | 清华大学 | Vehicle-mounted scale-free traffic accident scene quick surveying system based on binocular vision |
| CN102445186A (en) * | 2011-09-28 | 2012-05-09 | 中交第二公路勘察设计研究院有限公司 | Method for generating road design surface information by laser radar scan |
| CN102592454A (en) * | 2012-02-29 | 2012-07-18 | 北京航空航天大学 | Intersection vehicle movement parameter measuring method based on detection of vehicle side face and road intersection line |
| CN102661736A (en) * | 2012-05-17 | 2012-09-12 | 天津市星际空间地理信息工程有限公司 | Highway reorganization and expansion surveying method |
Non-Patent Citations (1)
| Title |
|---|
| 苏岳龙: "车载式三维信息快速采集及数据处理系统", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅱ辑》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108496176B (en) * | 2015-12-16 | 2021-12-31 | 法雷奥开关和传感器有限责任公司 | Method for identifying objects in the surrounding area of a motor vehicle, driver assistance system and motor vehicle |
| CN108496176A (en) * | 2015-12-16 | 2018-09-04 | 法雷奥开关和传感器有限责任公司 | The method for identifying the object in the peripheral region of motor vehicles, driver assistance system and motor vehicles |
| CN107784844A (en) * | 2016-08-31 | 2018-03-09 | 百度在线网络技术（北京）有限公司 | Intelligent traffic lamp system and its road environment detection method |
| CN109661667A (en) * | 2016-10-14 | 2019-04-19 | 富士通株式会社 | The retrograde detection device of vehicle and method, electronic equipment |
| CN107221175A (en) * | 2017-05-31 | 2017-09-29 | 深圳市鸿逸达科技有限公司 | A kind of pedestrian is intended to detection method and system |
| CN107423679A (en) * | 2017-05-31 | 2017-12-01 | 深圳市鸿逸达科技有限公司 | A kind of pedestrian is intended to detection method and system |
| CN107221175B (en) * | 2017-05-31 | 2020-10-27 | 深圳市鸿逸达科技有限公司 | Pedestrian intention detection method and system |
| CN110998687B (en) * | 2017-08-08 | 2022-08-02 | 索尼公司 | Control system and control method |
| CN110998687A (en) * | 2017-08-08 | 2020-04-10 | 索尼公司 | Control system and control method |
| TWI674210B (en) * | 2017-12-04 | 2019-10-11 | 財團法人資訊工業策進會 | System and method for detecting dangerous vehicle |
| CN109993976A (en) * | 2017-12-29 | 2019-07-09 | 技嘉科技股份有限公司 | Traffic accident monitors system and method |
| TWI651696B (en) * | 2017-12-29 | 2019-02-21 | 技嘉科技股份有限公司 | Systems and methods for monitoring traffic accident |
| CN108198419A (en) * | 2018-01-04 | 2018-06-22 | 山东华夏高科信息股份有限公司 | A kind of Intelligent road traffic safety hidden troubles removing system |
| CN108182811A (en) * | 2018-01-04 | 2018-06-19 | 山东华夏高科信息股份有限公司 | A kind of road safety perils detecting system |
| WO2020029013A1 (en) * | 2018-08-06 | 2020-02-13 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining traffic conditions |
| CN109243181A (en) * | 2018-09-21 | 2019-01-18 | 深圳市轱辘汽车维修技术有限公司 | Traffic accident method for early warning, device, terminal device and storage medium |
| CN109345829A (en) * | 2018-10-29 | 2019-02-15 | 百度在线网络技术（北京）有限公司 | Monitoring method, device, equipment and the storage medium of unmanned vehicle |
| CN113299102A (en) * | 2021-03-27 | 2021-08-24 | 宁波工程学院 | Underground parking lot navigation method, system and storage medium |
| CN116453065A (en) * | 2023-06-16 | 2023-07-18 | 云途信息科技(杭州)有限公司 | Road surface foreign matter throwing identification method and device, computer equipment and storage medium |
| CN116453065B (en) * | 2023-06-16 | 2023-09-19 | 云途信息科技(杭州)有限公司 | Road surface foreign matter throwing identification method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103325255B (en) | 2016-01-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103325255B (en) | The method of region transportation situation detection is carried out based on photogrammetric technology | |
| CN111551958B (en) | Mining area unmanned high-precision map manufacturing method | |
| CN105844222B (en) | The front vehicles collision warning systems and method of view-based access control model | |
| US10699567B2 (en) | Method of controlling a traffic surveillance system | |
| GB2596940A (en) | Systems and methods for vehicle navigation | |
| CN105427614A (en) | Model classification system and method | |
| CN104851295A (en) | Method and system for acquiring road condition information | |
| CN102467821A (en) | Road distance detection method based on video image and apparatus thereof | |
| CN103164958B (en) | Method and system for vehicle monitoring | |
| CN106327880B (en) | A kind of speed recognition methods and its system based on monitor video | |
| US20210208282A1 (en) | Detection device and detection system | |
| CN114248819A (en) | Railway intrusion foreign matter unmanned aerial vehicle detection method, device and system based on deep learning | |
| Kotha et al. | Potsense: Pothole detection on indian roads using smartphone sensors | |
| Yao et al. | Developing operating mode distribution inputs for MOVES with a computer vision–based vehicle data collector | |
| CN114067287A (en) | Foreign matter identification and early warning system based on vehicle side road side data perception fusion | |
| Zhang et al. | Machine learning and computer vision-enabled traffic sensing data analysis and quality enhancement | |
| CN104485002B (en) | A kind of vehicle detection system based on PSD | |
| CN111709354A (en) | Method and device for identifying target area, electronic equipment and road side equipment | |
| Jinturkar et al. | Vehicle detection and parameter measurement using smart portable sensor system | |
| Kurz et al. | Automatic traffic monitoring with an airborne wide-angle digital camera system for estimation of travel times | |
| Arai et al. | Method for traffic flow estimation using on-dashboard camera image | |
| CN111709356B (en) | Method and device for identifying target area, electronic equipment and road side equipment | |
| CN111310643B (en) | Vehicle counting method and device based on point cloud data and electronic equipment | |
| Han et al. | Real-time detection of vehicles for advanced traffic signal control | |
| CN111709355B (en) | Method and device for identifying target area, electronic equipment and road side equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160120 Termination date: 20210629 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |