CN109147393A - Vehicle lane change detection method based on video analysis - Google Patents
Vehicle lane change detection method based on video analysis Download PDFInfo
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- CN109147393A CN109147393A CN201811212517.1A CN201811212517A CN109147393A CN 109147393 A CN109147393 A CN 109147393A CN 201811212517 A CN201811212517 A CN 201811212517A CN 109147393 A CN109147393 A CN 109147393A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/48—Extraction of image or video features by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
- G06T2207/30256—Lane; Road marking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
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Abstract
The vehicle lane change detection method based on video analysis that the invention discloses a kind of, including monitoring image is obtained, and pre-processed；Using two classification SVM models, judge whether there is vehicle in image；It is that tracking starting point is tracked with vehicle detection result, detects each vehicle region, extract the feature of each vehicle, if the feature difference of tracking is less than the threshold value of setting, track success, on the contrary tracking failure；Lane line is identified, position coordinates of the lane line in video image are obtained；According to the coordinate of lane line, from left to right to driveway partition region, coordinate section is set, tracking vehicle centroid point then determines the vehicle lane change when vehicle centroid point is in different lane regions.Detection efficiency and detection accuracy can be greatly improved.
Description
Technical field
The present invention relates to a kind of vehicle lane change detection methods, examine more particularly to a kind of vehicle lane change based on video analysis
Survey method.
Background technique
With increasing rapidly for automobile demand amount, the automobile production quantity in China has occupied first place in the world.Trip is convenient
The problems such as congested in traffic, traffic accident takes place frequently also is brought simultaneously.Automobile quantity is big, and vehicle violation accordingly increases, break in traffic rules and regulations
It is the one of the major reasons that congested in traffic, traffic accident takes place frequently.There are many vehicle violation type, big in vehicle flowrate, and running speed
There are great hidden trouble of traffic for random change lane on higher road.It will lead to traffic accident.Random lane change is to cause traffic
The one of the major reasons of accident, the increasingly concern by government and various circles of society.Therefore how effectively to detect and identify vehicle
Illegal lane change, prevention and reducing are lost caused by traffic accident, it has also become urgently to be resolved one of current traffic management department
Urgent task.
Traditional vehicle lane change detection method is by traffic police's field observation or to carry out artificial interpretation to monitor video, this
Kind method is both time- and labor-consuming and detection accuracy is lower.Also there is the detection method based on image analysis at present, but does not all have
The following problem of very good solution:
(1) different vehicle color, difference in size, etc. factors influence detection accuracy；
(2) weather, illumination influence of the variation to testing result；
(3) influence of the variation of car speed to testing result.
Summary of the invention
In order to solve above-mentioned technical problem, the vehicle lane change detection based on video analysis that the present invention provides a kind of
Method can greatly improve detection efficiency and detection accuracy.
The technical scheme is that
A kind of vehicle lane change detection method based on video analysis, comprising the following steps:
S01: monitoring image is obtained, and is pre-processed；
S02: using two classification SVM models, judge whether there is vehicle in image；
S03: it is that tracking starting point is tracked with vehicle detection result, detects each vehicle region, extract each vehicle
Feature, if tracking feature difference be less than setting threshold value, track success, on the contrary tracking failure；
S04: identifying lane line, obtains position coordinates of the lane line in video image；
S05: according to the coordinate of lane line, from left to right to driveway partition region, coordinate section is set, tracks vehicle centroid
Point then determines the vehicle lane change when vehicle centroid point is in different lane regions.
In preferred technical solution, the step S01 gaussian filtering is to image noise reduction processing, filter function are as follows:
Wherein, at a distance from center pixel in neighborhood, σ is other pixels in neighborhood that square respectively indicate of the quadratic sum y of x
Standard deviation.
In preferred technical solution, if track the frequency of failure in the step S03 and be greater than the threshold value of setting, without with
Track.
In preferred technical solution, the feature of the vehicle extracted in the step S03 includes center of mass point, area and external square
Shape.
In preferred technical solution, the step S04 the following steps are included:
Rectangular coordinate system is established to road；
By hough change detection straight line, straight line is ρ=xcos θ+ysin θ, and ρ is distance of the point on straight line to origin,
θ is vertical line and x-axis angle；
Obtain position coordinates of the lane line in video image.
Compared with prior art, the invention has the advantages that
This method can identify that vehicle tracking, vehicle detects in certain region to multiple vehicles, and verification and measurement ratio is high, energy
Under the traffic environment of various complexity to vehicle whether lane change, multiple lane change, can be very good to exclude weather, illumination, on lane
The influence to lane detection such as pedestrian, sundries, shade, and by a large amount of test display, this method accuracy be 92% with
On, can satisfy under different kinds of roads environment, to vehicle whether the testing requirements of lane change and lane change violating the regulations.
Detailed description of the invention
The invention will be further described with reference to the accompanying drawings and embodiments:
Fig. 1 is that the present invention is based on the flow charts of the vehicle lane change detection method of video analysis.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, With reference to embodiment and join
According to attached drawing, the present invention is described in more detail.It should be understood that these descriptions are merely illustrative, and it is not intended to limit this hair
Bright range.In addition, in the following description, descriptions of well-known structures and technologies are omitted, to avoid this is unnecessarily obscured
The concept of invention.
Embodiment:
With reference to the accompanying drawing, presently preferred embodiments of the present invention is described further.
As shown in Figure 1, the present invention is based on the vehicle lane change detection methods of video analysis, comprising the following steps:
Step 1: obtaining video from monitoring camera, and image is obtained from video, and the image more obtained is located in advance
Reason, such as with gaussian filtering to image noise reduction processing, filter function are as follows:
Wherein, the quadratic sum y of x square respectively indicate be in neighborhood in other pixels and neighborhood center pixel away from
From what σ was represented is standard deviation.
Step 2: using two classification SVM models in vehicle detection, judge whether there is vehicle in image:
F (x)=β+wTx；
Wherein, β is the constant obtained by training, and w is to be by what is obtained after the supporting vector weighted sum trained
Number vector.X is the feature vector extracted on image to be classified, if f (x) is greater than 0, image to be classified is vehicle, otherwise
It is not vehicle.
It detects each vehicle region, extracts the center of mass point of each vehicle, area, the features such as boundary rectangle.It detects for the first time
As a result as detection target { trackj| j=1,2,3 ..., M }, testing result { blob lateri| i=1,2 ..., N } then it is used for
Tracking.It is preliminary to exclude non power driven vehicle.The non-motor vehicles such as motorcycle, bicycle are generally smaller than motor vehicle, therefore trackiFace
Product feature is less than threshold value areaiWhen do not detect.In addition, being taken the photograph because of the installation site of camera when vehicle region is approached or sailed out of
When as head, detect that the area features of vehicle differ greatly, so close to the track of image edgeiAlso it does not detect.trackiMatter
The heart does not enter into intrusion detection range and does not detect yet.
Work as blobiCenter of mass point centeri, area areaiWith trackiCenter of mass point centerj(x, y), area areaj(x,
Y) difference, which all meets, is no more than threshold value TcenterWith threshold value Tarea, then it is assumed that it tracks successfully.trackjThe tracking frequency of failure
inactivejIt is initialized as 0, if tracking successfully, updates trackjFeature, otherwise inactivejFrom increasing 1；If trackj's
Track frequency of failure inactivejFrequency of failure threshold value T is tracked more than maximum1, then it is assumed that the vehicle has been driven out to camera shooting model
It encloses, can delete and not have to track again.
Step 3: lane line is identified, comprising the following steps:
Rectangular coordinate system is established to road；
By hough change detection straight line, straight line is ρ=xcos θ+ysin θ, and ρ is distance of the point on straight line to origin,
θ is vertical line and x-axis angle；
Obtain position coordinates of the lane line in video image.
In being identified by Hough transform to lane line, it is assumed that have straight line in rectangular coordinate system, if on straight line
The distance of point to origin be ρ, vertical line and x-axis angle are θ, this straight line can indicate are as follows:
ρ=xcos θ+ysin θ
The straight line is only a point (ρ, θ) in polar coordinate system space, and Hough transform is by one in rectangular coordinate system
Straight line is mapped to a point in polar coordinate space.
Being mapped in polar coordinates in rectangular coordinate system by all straight lines at (x, y) is one by (ρ, θ) point
Sine curve, the certain point in rectangular coordinate system correspond in polar coordinates the point being located on this sine curve.So right angle
If it is exactly several sine curves that doing in coordinate system, which is mapped in polar coordinates, if these points constitute one in rectangular coordinate system
Straight line, is set as (ρ ', θ '), and corresponding linear equation indicates are as follows:
ρ '=xcos θ '+ysin θ '
Position coordinates of the traffic lane line in video image are obtained by hough change detection straight line.
Step 4: the region that camera was photographed obtains lane line coordinates according to the method for step 3, and every lane line has
Two apex coordinates from left to right carry out division region to lane, and coordinate section is arranged.It is moved by the method for step 2
The center-of-mass coordinate (cx, cy) of vehicle detects vehicle centroid point start bit by the function pointPolygonTest in openCV
Which set in the region in lane.The center of mass point is tracked, real-time coordinates is obtained, obtains vehicle lane change track, such as finds vehicle matter
Heart point appears in other lane regions, then determines the vehicle lane change.If the vehicle center of mass point coordinate goes out within the time of setting
In present multiple vehicles to region, then it is determined as lane change violating the regulations.
Step 5: after obtaining testing result, picture concerned or video are saved.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (5)
1. a kind of vehicle lane change detection method based on video analysis, which comprises the following steps:
S01: monitoring image is obtained, and is pre-processed；
S02: using two classification SVM models, judge whether there is vehicle in image；
S03: it is that tracking starting point is tracked with vehicle detection result, detects each vehicle region, extract the spy of each vehicle
Sign tracks success if the feature difference of tracking is less than the threshold value of setting, otherwise tracking failure；
S04: identifying lane line, obtains position coordinates of the lane line in video image；
S05: according to the coordinate of lane line, from left to right to driveway partition region, being arranged coordinate section, track vehicle centroid point,
When vehicle centroid point is in different lane regions, then the vehicle lane change is determined.
2. the vehicle lane change detection method according to claim 1 based on video analysis, which is characterized in that the step
S01 gaussian filtering is to image noise reduction processing, filter function are as follows:
Wherein, for other pixels in neighborhood that square respectively indicate of the quadratic sum y of x at a distance from center pixel in neighborhood, σ is standard
Difference.
3. the vehicle lane change detection method according to claim 1 based on video analysis, which is characterized in that the step
If track threshold value of the frequency of failure greater than setting in S03, without tracking.
4. the vehicle lane change detection method according to claim 1 based on video analysis, which is characterized in that the step
The feature of the vehicle extracted in S03 includes center of mass point, area and boundary rectangle.
5. the vehicle lane change detection method according to claim 1 based on video analysis, which is characterized in that the step
S04 the following steps are included:
Rectangular coordinate system is established to road；
By hough change detection straight line, straight line is ρ=xcos θ+ysin θ, and ρ is distance of the point on straight line to origin, and θ is
Vertical line and x-axis angle；
Obtain position coordinates of the lane line in video image.
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| CN113269165A (en) * | 2021-07-16 | 2021-08-17 | 智道网联科技(北京)有限公司 | Data acquisition method and device |
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| CN110264512A (en) * | 2019-06-28 | 2019-09-20 | 清华大学苏州汽车研究院（吴江） | Lane side distance detecting method and device based on video analysis |
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| CN111539371A (en) * | 2020-05-06 | 2020-08-14 | 腾讯科技（深圳）有限公司 | Vehicle control method, device, equipment and storage medium |
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| CN114463416A (en) * | 2021-12-31 | 2022-05-10 | 浙江大华技术股份有限公司 | Vehicle lane change detection method and device, electronic equipment and storage medium |
| CN114898325A (en) * | 2022-07-12 | 2022-08-12 | 深圳市城市交通规划设计研究中心股份有限公司 | Vehicle dangerous lane change detection method and device and electronic equipment |
| CN114898325B (en) * | 2022-07-12 | 2022-11-25 | 深圳市城市交通规划设计研究中心股份有限公司 | Vehicle dangerous lane change detection method and device and electronic equipment |
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Application publication date: 20190104 |