CN103810854B - A kind of based on the artificial intelligent transportation parameter detection method demarcated - Google Patents
A kind of based on the artificial intelligent transportation parameter detection method demarcated Download PDFInfo
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Abstract
The present invention relates to a kind of based on the artificial intelligent transportation parameter detection method demarcated, comprising: set up traffic video database; ViPER software is utilized manually to demarcate traffic video; The parameter of demarcation is saved as the file of XML format, and derived; Data analysis is carried out to XML file, obtains required traffic parameter; Improve traffic video database; Treat verification algorithm to detect.The present invention by artificial cognition and demarcate vehicle/pedestrian/traffic sign in traffic video time-positional information, by computing machine automatic acquisition traffic parameter, compared with the traffic parameter intelligent algorithm based on computer vision, substantially increase the precision of traffic parameter.Establish the detection platform of multiple traffic parameter intelligent algorithm, can treat that verification algorithm carries out many scenes, multiparameter, checking in all directions detect.Detection method accurately and reliably, has certain directive function to the Improvement and perfection of detected parameter algorithm.
Description
Technical field
The invention belongs to machine vision and field of intelligent control, is a kind of method utilizing computer technology, image processing techniques, database technology etc. to detect intelligent transportation parameter.
Background technology
Along with the perfect gradually of highway traffic infrastructure and the increase of private car quantity, road traffic problem is day by day serious.In recent years, frequent accidents, the Loss of Life and property that road traffic accident causes more and more causes the concern of people.According to statistics: 2008, there is road traffic accident 265204 in the whole nation altogether, and cause 73484 people dead, 304919 people are injured, direct property loss 10.1 hundred million yuan; 2009, there is road traffic accident 238351 in the whole nation altogether, and cause 67759 people dead, 275125 people are injured, direct property loss 9.1 hundred million yuan; 2010, there is road traffic accident 3906164 in the whole nation altogether, and cause 65225 people dead, 254075 people are injured, direct property loss 9.3 hundred million yuan.In order to reduce the Loss of Life and property that road traffic accident causes to greatest extent, in the past few decades, domestic and international expert and scholar are actively developing the research work about intelligent transportation system (ITS).
In recent years, numerous intelligent transportation algorithm based on computer vision arises at the historic moment, and they use the technology such as computing machine and image procossing, complete the automatic detection to comprising the traffic parameters such as vehicle number, the speed of a motor vehicle, vehicle.Algorithm is different, and precision is also different.Although the accuracy rate that various intelligent algorithm is announced is mostly more than 90%, but owing to going back unified, accurate, the reliable verification platform of neither one and detection method up to now, cannot verifying that whether the accuracy that various algorithm is announced is credible, also with regard to being difficult to, judge being compared to the quality of various algorithm.
Summary of the invention
For the problems referred to above, the present invention proposes a kind of based on the artificial intelligent transportation parameter detection method demarcated, based on artificial data of demarcating, obtain traffic parameter accurately, and the traffic parameter that itself and intelligent algorithm to be detected obtain is contrasted, obtain the accuracy of this algorithm, thus evaluate the quality of this algorithm performance.
Hardware device involved in the present invention comprises PC, video camera and corresponding capture card.The video of video camera shooting imports PC, and is converted into the video file of general format, as MPG form.Artificial demarcation part need apply open source software ViPER, this software can label target unit is residing in video frame by frame manually position, the present invention apply this software carry out traffic unit (vehicle/pedestrian/traffic sign) time-position data demarcates.
Based on the artificial intelligent transportation parameter detection method demarcated, comprise the following steps:
Step 1, sets up traffic video database.
Collect the traffic video under many scenes, varying environment, visual classification is as follows:
Different location: through street, intersection, high speed crossing ring road etc.;
Different periods: morning peak, evening peak, daytime, at dusk etc.;
Varying environment: shadow-free, illumination shade is obvious, mist, sleety weather etc.;
Different target: vehicle, pedestrian, traffic sign etc.;
Every section of duration 10 ~ 60 minutes not etc., can not meet the checking demand of many algorithms.
Step 2, utilizes ViPER software manually to demarcate traffic video.
Step 2.1, is loaded into traffic video to be calibrated.
Step 2.2, sets the parameter that will demarcate, comprises title, callout box kind etc.
Step 2.3, artificial each unit demarcated in traffic video time-positional information.
The information of artificial demarcation comprises: unit number; Classification: 1-vehicle, 2-pedestrian, 3-traffic sign; The frame number that unit continues in video; Upper left corner point coordinate (X, Y) of unit area; The transverse width Width of unit area, longitudinal height H eight.
Step 3, derives artificial data of demarcating.
The data of demarcating in ViPER software are saved as the file of XML format, and derived.
Step 4, carries out data analysis to XML file described in step 3, the traffic parameter needed for acquisition.
Step 4.1, forms vehicle travel track.
Read demarcation information, the rectangle frame center (X+Width/2, Y+Height/2) be made up of is considered as vehicle position, links the vehicle location of each time point X, Y, Width, Height, form vehicle travel track.
Step 4.2, calculates vehicle number.
(1) according to video setting number of track-lines and virtual coil, coil width and track are with wide, and length is not less than a length of wagon.
(2) according to the geometric relationship of vehicle and virtual coil, divided lane vehicle count is carried out.
As shown in Figure 1, quadrilateral ABCD is the virtual coil of setting, and vehicle V sails CD limit into by AB limit and rolls away from.There is geometric relationship VA+VB-AB >=0, VC+VD-CD >=0 in vehicle location and virtual coil four summits.The corresponding vehicle of minimum point of VA+VB-AB enters the moment of this virtual coil, and the corresponding vehicle of minimum point of VC+VD-CD rolls the moment of virtual coil, the moment that can judge the behavior that vehicle sailed or rolled away from coil into accordingly and enter and roll away from away from.
If certain car completes same virtual coil and sails the behavior of rolling away from into, carry out once to the vehicle count in this track.This track vehicle number+1.
If certain vehicle sails certain virtual coil into, but do not roll any virtual coil away from, then think that this car travels abnormal, do not count.
If certain vehicle sails certain virtual coil into, but roll away from from another virtual coil, then think that this car carries out changing behavior, the virtual coil counting+1 that it rolls away from.
Step 4.3, calculates following distance, average following distance.
According to camera calibration, obtain the physical length corresponding to road in video.Subtracted each other by adjacent rectangle frame coordinate position and obtain following distance, average following distance is the following distance average in statistics a period of time.
Step 4.4, calculates the speed of a motor vehicle, average speed.
According to camera calibration, obtain drawn actual range corresponding to virtual coil, obtain according to method described in step 4.2 moment that each car entered and rolled away from virtual coil, obtain Vehicle Speed by the distance/time.In statistics a period of time, the speed of all vehicles through virtual coil, obtains its average velocity.
Step 4.5, calculates vehicle queue length.
Vehicle queue is a kind of traffic shock wave, therefore queue length is different from the existence judgement of vehicle, not only relevant, also relevant to its historical information to the current location of each car in scene, velocity information, only can not obtain queue length accurately from a certain frame single image or video.The present invention is based on the artificial calibrated and calculated vehicle queue length of multiframe, long-time section, concrete grammar is as follows:
Setting stop line position, each car utilizing step 1 to obtain time-positional information draws it apart from 'STOP' line ahead and the relation curve of time, as shown in Figure 2, every bar line correspondence car.Arrive the queuing vehicle of intersection through traveling-parking waiting-continuation traveling 3 stage, parking is called flex point with the separation started.The flex point of each car in matching track obtains stop wave A (t) in whole track and startup ripple B (t), i.e. traffic shock wave, as shown in Figure 3, and the corresponding stop line position of initial point.Relation according to traffic shock wave and queue length calculates queue length and stop delay:
QL(t)＝TL(t)-HL(t)
In formula, t is the time, and QL (t) is queue length, and TL (t) is tail of the queue and 'STOP' line ahead, and HL (t) is team's head and 'STOP' line ahead.
Step 4.6, obtains traffic sign positional information.
Read the road signs information of Vehicular video mark, obtain traffic sign in the position in each moment, pass through camera calibration, simulate the length relation in actual range correspondence image, actual range information is converted into, in real time the distance of display between this traffic sign and driving vehicle by the positional information of traffic sign.
Step 4.7, forms pedestrian's track according to the method for step 4.1.
Step 5, improves traffic video database.
Every section of traffic video in step 1 is manually demarcated according to method described in step 2 and step 3, then calculate the traffic parameter in every section of video according to method described in step 4, the artificial nominal data of original video and correspondence thereof and traffic parameter form the traffic video database detected for algorithm jointly.
Step 6, treats verification algorithm and detects.
One or more snippets video in selecting video database, applies the above-mentioned traffic parameter of algorithm measurement to be verified, is compared by the traffic parameter in measurement result and database, the accuracy of computation and measurement, judge the accuracy of algorithm to be verified thus.
Compared with prior art, the present invention has the following advantages:
1. the present invention by artificial cognition and demarcate vehicle/pedestrian/traffic sign in traffic video time-positional information, by computing machine automatic acquisition traffic parameter, compared with the traffic parameter intelligent algorithm based on computer vision, substantially increase the precision that traffic parameter calculates; Compared with the method for pure manual record, efficiency is higher, and is easy to store, and also can avoid the error that some artificial processing eases occur.
2. the present invention's application comprises the database of traffic video and artificial nominal data and traffic parameter, can carry out many scenes, multiparameter, checking in all directions detect to traffic parameter intelligent algorithm to be verified.Owing to employing high precision traffic parameter, ensure that the accuracy that checking detects and reliability.The Improvement and perfection of detection method of the present invention to detected parameter algorithm has certain directive function.
Accompanying drawing explanation
Fig. 1 is virtual coil method of counting schematic diagram;
Fig. 2 is that queuing vehicle is apart from 'STOP' line ahead and the relation curve of time;
Fig. 3 is traffic shock wave and queue length relation schematic diagram;
Fig. 4 is the process flow diagram of method involved in the present invention;
Fig. 5 be artificial mark in video vehicle time-positional information schematic diagram;
Fig. 6 is with the video schematic diagram of markup information and virtual coil.
Embodiment
Below in conjunction with drawings and Examples, the present invention will be further described.
Application the present invention detect the image processing algorithm that a kind of divided lane counts.Embodiment realizes on the PC installing VC2008 and OpenCV2.3.1.
The process flow diagram of the method for the invention as shown in Figure 4, comprises the following steps:
Step 1, sets up traffic video database.
Database at least comprises the traffic video of 4 kinds of varying environments, and file layout is MPG.
Step 2, utilizes ViPER software manually to demarcate traffic video.
Step 2.1, is loaded into one section of vehicle and by sunlight the traffic video yinying.mpg producing shade.
Step 2.2, set the parameter that will demarcate, the vehicle title of mark is set as Vehicle, and callout box kind is Bbox (rectangle frame).
Step 2.3, manually marks out the position residing for vehicle each moment.As shown in Figure 5.
Step 3, derives artificial data of demarcating.
The parameter of demarcating in ViPER software saves as the file of XML format, is preserved and derives.Export name is called yinying-mpg.xml.Save in file the unit of all artificial demarcation time-positional information
For a certain car, XML data file comprises following parameter:
Car number ID=0;
Classification KIND=1 (vehicle);
The frame number framespan=1:45 that vehicle continues in video;
Detailed data Data: comprise the upper left corner point coordinate of each two field picture vehicle region, the width of unit area and height.
Step 4, by have unit in video time-XML file of positional information carries out data analysis, to obtain required traffic parameter---vehicle number (divided lane).
Step 4.1, be 4 according to video setting number of track-lines, and set virtual coil, coil width and track are with wide, and length is not less than a length of wagon.As shown in Figure 6.
Step 4.2, reads XML file and obtains divided lane count results.
Four track count results are respectively 3,16,27,7.
Step 5, is loaded into other video-frequency band, carries out artificial demarcation and parameter calculating respectively, improve original video data storehouse according to method described in step 2 ~ 5.Wherein, the divided lane of the traffic video xue.mpg in one section of snow sky is counted as 10,22,26,9, and one section is counted as 18,30,39,32 without the divided lane of the traffic video normal.mpg of any environmental interference clearly substantially.
Step 6, above-mentioned 3 sections of identical videos runs algorithm to be verified, and obtains four track count results.Video yinying.mpg is 3,15,27,6; Video xue.mpg is 3,11,10,3; Video normal.mpg is 18,30,40,32.The traffic parameter that the true traffic parameter of every section of video and algorithm to be verified record is contrasted, detects algorithm accuracy to be verified.
For video yinying.mpg, the process accuracy testing result of algorithm to be verified is 94.87%, substantially meets accuracy requirement.
For video xue.mpg, the process accuracy testing result of algorithm to be verified is 37.95%, cannot meet accuracy requirement.
For video normal.mpg, the process accuracy testing result of algorithm to be verified is 99.38%, meets accuracy requirement.
Can draw the following conclusions according to testing result: algorithm to be verified affects not quite by illumination and shade etc., and under other atrocious weather environment such as sleet, the precision of algorithm declines greatly, cannot meet accuracy requirement.
Claims (4)
1., based on the artificial intelligent transportation parameter detection method demarcated, it is characterized in that, comprise the following steps:
Step 1, sets up traffic video database;
Collect the traffic video under many scenes, varying environment, visual classification is as follows:
Different location: through street, intersection, high speed crossing ring road;
Different periods: morning peak, evening peak, daytime, at dusk;
Varying environment: shadow-free, illumination shade is obvious, mist, sleety weather;
Different target: vehicle, pedestrian, traffic sign;
Every section of duration 10 ~ 60 minutes not etc., can not meet the checking demand of many algorithms;
Step 2, utilizes ViPER software manually to demarcate traffic video;
Step 2.1, is loaded into traffic video to be calibrated;
Step 2.2, sets the data that will demarcate, comprises title, callout box kind;
Step 2.3, artificial demarcate each unit in traffic video and vehicle/pedestrian/traffic sign time-positional information;
Step 3, derives artificial data of demarcating;
The data of demarcating in ViPER software are saved as the file of XML format, and derived;
Step 4, carries out data analysis to XML file described in step 3, the traffic parameter needed for acquisition;
Step 4.1, forms vehicle travel track;
Read demarcation information, the rectangle frame center (X+Width/2, Y+Height/2) be made up of is considered as vehicle position, links the vehicle location of each time point X, Y, Width, Height, form vehicle travel track; Wherein, the upper left corner point coordinate that (X, Y) is unit region, Width, Height are respectively the transverse width of unit area and longitudinal height;
Step 4.2, calculates vehicle number;
Step 4.3, calculates following distance, average following distance;
According to camera calibration, obtain the physical length corresponding to road in video; Subtracted each other by adjacent rectangle frame coordinate position and obtain following distance, average following distance is the following distance average in statistics a period of time;
Step 4.4, calculates the speed of a motor vehicle, average speed;
According to camera calibration, obtain drawn actual range corresponding to virtual coil, obtain the moment that each car entered and rolled away from virtual coil, obtain Vehicle Speed by the distance/time; In statistics a period of time, the speed of all vehicles through virtual coil, obtains its average velocity;
Step 4.5, calculates vehicle queue length;
Step 4.6, obtains traffic sign positional information;
Read the road signs information of Vehicular video mark, obtain traffic sign in the position in each moment, by camera calibration, simulate the length relation in actual range correspondence image, be converted into actual range information by the positional information of traffic sign;
Step 4.7, forms pedestrian's track according to the method for step 4.1;
Step 5, improves traffic video database;
Every section of traffic video described in step 1 is manually demarcated according to method described in step 2 and step 3, then calculate the traffic parameter in every section of video according to method described in step 4, the artificial nominal data of original video and correspondence thereof and traffic parameter form the traffic video database for algorithm evaluation jointly.
2. a kind of intelligent transportation parameter detection method based on artificial demarcation according to claim 1, it is characterized in that, the information that described step 2.3 is manually demarcated comprises: unit number; Classification: 1-vehicle, 2-pedestrian, 3-traffic sign; The frame number that unit continues in video; Upper left corner point coordinate (X, Y) of unit area; The transverse width Width of unit area, longitudinal height H eight.
3. according to claim 1 a kind of based on the artificial intelligent transportation parameter detection method demarcated, it is characterized in that, the method that described step 4.2 calculates vehicle number is further comprising the steps of:
(1) according to video setting number of track-lines and virtual coil, coil width and track are with wide, and length is not less than a length of wagon;
(2) according to the geometric relationship of vehicle and virtual coil, divided lane vehicle count is carried out;
Suppose that quadrilateral ABCD is the virtual coil of setting, vehicle V sails CD limit into by AB limit and rolls away from; There is geometric relationship VA+VB-AB >=0, VC+VD-CD >=0 in vehicle location and virtual coil four summits; The corresponding vehicle of minimum point of VA+VB-AB enters the moment of this virtual coil, and the corresponding vehicle of minimum point of VC+VD-CD rolls the moment of virtual coil, the moment that can judge the behavior that vehicle sailed or rolled away from coil into accordingly and enter and roll away from away from;
If certain car completes same virtual coil and sails the behavior of rolling away from into, carry out once to the vehicle count in this track; This track vehicle number+1;
If certain vehicle sails certain virtual coil into, but do not roll any virtual coil away from, then think that this car travels abnormal, do not count;
If certain vehicle sails certain virtual coil into, but roll away from from another virtual coil, then think that this car carries out changing behavior, the virtual coil counting+1 that it rolls away from.
4. a kind of intelligent transportation parameter detection method based on artificial demarcation according to claim 1, is characterized in that, described step 4.5 is based on the artificial calibrated and calculated vehicle queue length of multiframe, long-time section, and concrete grammar is as follows:
Setting stop line position, each car utilizing step 2 to obtain time-positional information draws it apart from 'STOP' line ahead and the relation curve of time, every bar line represents a car; Arrive the queuing vehicle of intersection through traveling-parking waiting-continuation traveling 3 stage, parking is called flex point with the separation started; The flex point of each car in matching track obtains the stop wave in whole track and startup ripple, i.e. traffic shock wave; Relation according to traffic shock wave and queue length calculates queue length and stop delay:
QL(t)＝TL(t)-HL(t)
In formula, t is the time, and QL (t) is queue length, and TL (t) is tail of the queue and 'STOP' line ahead, and HL (t) is team's head and 'STOP' line ahead.
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| CN107301776A (en) * | 2016-10-09 | 2017-10-27 | 上海炬宏信息技术有限公司 | Track road conditions processing and dissemination method based on video detection technology |
| CN108447261B (en) * | 2018-04-04 | 2020-09-18 | 迈锐数据（北京）有限公司 | Vehicle queuing length calculation method and device based on multiple modes |
| CN109522825A (en) * | 2018-10-31 | 2019-03-26 | 蔚来汽车有限公司 | The Performance Test System and its performance test methods of visual perception system |
| CN111141311B (en) * | 2019-12-31 | 2022-04-08 | 武汉中海庭数据技术有限公司 | Evaluation method and system of high-precision map positioning module |
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