CN110097571B - Quick high-precision vehicle collision prediction method - Google Patents
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Abstract
A fast and high-precision vehicle collision prediction method comprises the following steps: firstly, generating a motion interaction field for a video frame input at a certain moment by using a Farnesback algorithm and a Gaussian difference kernel function, expressing the symmetry of the motion interaction field by using the area difference of positive and negative areas, determining an abnormal value at the moment by adopting a linear regression model, and finally predicting whether a vehicle collides in a hard threshold mode.
Description
Technical Field
The invention belongs to the field of intelligent traffic, and relates to a rapid and high-precision vehicle collision prediction method which is particularly suitable for predicting road vehicle collision in real time and all weather.
Background
Many research groups at home and abroad try to study an automatic detection method of a vehicle collision accident through computer vision and pattern recognition technology. SiyuXia et al propose a method based on low-rank matrix approximation to rapidly and effectively automatically measure traffic accidents in video. It first divides each frame into non-overlapping blocks associated with different weights, then extracts the motion matrix of the video segment, and after using a low rank matrix approximation to associate a normal traffic scene with a set of motion subspaces, identifies traffic incidents at the moment of increasing approximation errors. Yu Chen et al propose a classifier-based monitoring method by observing the last second of video before a collision of a motor vehicle as a detection target. It first extracts local motion information features from the time domain using the OF-SIFT algorithm for motion detection, and then classifies traffic accidents using an Extreme Learning Machine (ELM) classifier as a basic classifier. Li Yuee and the like are used for obtaining a traffic accident probability formula by extracting parameters such as smoke, fragments and change of the speed of a moving target in a traffic monitoring video and carrying out formula fitting by combining with a maquardt method, calculating the accident probability, and judging whether the vehicle collides according to the probability. The baya et al analyzes some specific states of the vehicle in the video, such as size, position, etc., and judges whether the two targets have overlapping areas according to the movements, thereby judging whether the vehicle collides. Liu Weiqi and the like calculate a bounding box of a vehicle by using an OBB algorithm, then project a ground plane of the bounding box to obtain a rectangular two-dimensional bounding box of the vehicle, and then detect whether the rectangles intersect on the same plane so as to judge whether the vehicle collides. Yin Chune and the like propose an adaptive threshold highway traffic accident detection method based on a wavelet equation, and the threshold is dynamically and adaptively adjusted based on the change of traffic running states, so that a relatively high accident detection rate is achieved. Yin Hongpeng by intelligent analysis, detecting the motion of the vehicle by background difference method, tracking the motion vehicle by mean shift algorithm, and weighting and fusing the obtained parameters such as vehicle position, speed and motion direction to detect the vehicle behavior (collision, stop running, etc. of the vehicle)
The algorithm well realizes traffic accident detection, but has the problems of inaccurate target detection, poor tracking effect, low collision discrimination accuracy and the like.
Atev and the like adopt a plurality of cameras to monitor the same intersection at the same time, acquire a motion state vector of a target after data fusion, and establish a vehicle collision prediction algorithm by utilizing a vehicle track extrapolation assumption for realizing collision detection. Lauresh et al have attempted to establish a set of severity indicators that can measure traffic interaction events throughout the process, including distance collision time (TTC), time of dominance (TAdv), gap Time (TG), speed, etc., based on which traffic collisions are classified and identified according to the severity of the event throughout the process. Ismail and the like detect, track and classify pedestrians and vehicles in traffic scenes, automatically calculate severity indexes by means of extrapolation assumptions, and identify collision between the pedestrians and the vehicles accordingly. Zhang Fangfang microscopic traffic parameters such as track, speed, acceleration and the like of the collision vehicles are extracted from the video image sequence, a majority of traffic collision judging methods are established, and automatic detection of motor vehicle traffic collision videos under simple traffic conditions is primarily realized. Qu Zhaowei and the like are used for predicting the moving target track by introducing a GM (1, 1) gray model on the basis of moving target detection and tracking, and realizing automatic judgment of traffic conflict at a signalless intersection on the basis of establishing two conditions of traffic conflict judgment. Hu and the like learn the motion mode of the vehicle by using a fuzzy self-organizing neural network algorithm, match and predict the motion trail of the vehicle according to the motion mode, and finally predict the accident by calculating the occurrence probability of collision between vehicles. Saunier et al set up a video-based automatic analysis method of cross security, learn the track prototype through the longest public subsequence (LCSS) algorithm, calculate the collision possibility between vehicles on this basis, judge the traffic conflict.
Although research on traffic conflict technology based on videos has achieved some staged results, most of the existing conflict judging methods simply and directly apply microscopic traffic data collected by videos, and are often finally attributed to the traditional traffic conflict judging thought.
The patent aims to provide the rapid and high-precision vehicle collision prediction method which has the advantages of small calculated amount, high calculation speed, strong noise resistance and high detection precision and can predict the vehicle collision traffic accidents in real time all weather.
Disclosure of Invention
1. A rapid and high-precision vehicle collision prediction method is characterized by comprising the following steps:
step one, constructing a motion interaction field F 2 (x, y) according to the following steps:
firstly, inputting a video frame, and performing downsampling or upsampling on an original image frame to obtain an image frame f (x, y) with the size of MxN;
secondly, extracting the motion information of a target object in the video sequence by using a farnebback optical flow algorithm: processing the ith pixel value f (x i ,y i ) Obtaining the velocity in the x direction
And speed in y direction>
Thereby obtaining an optical flow field V with the size of MxN 0x And V 0y ；
Thirdly, downsampling an optical flow field to obtain optical flow fields Vx and Vy with the size of M/10 XN/10;
fourth, vx and Vy are applied to kernel function K to obtain motion interaction field F with the size of M/10 XN/10 1 (x, y) having the expression:
wherein K is a kernel function, and the expression is:
in the above-mentioned method, the step of,
is forward center position +.>
For the rearward center position, k is the width sigma and the center is (x c ,y c ) The expression of the Gaussian kernel function is as follows:
fifth step, for F 1 (x, y) obtaining a motion interaction field F with the size of 2 Mx 2N by bicubic interpolation 2 (x,y)。
Step two, obtaining the abnormal value of the motion interaction field at the time t by using the area difference method
The method comprises the following steps:
first, utilize threshold th 1 The motion interaction field F obtained in the last step is obtained 2 The absolute value of the pixel value in (x, y) is greater than th 1 Sum S of positive and negative area 1 、S 2 ；
Second, calculating the absolute value M of the positive and negative area difference abn The expression is:
M abn ＝|S 1 -S 2 |；
third step, smoothing M using autoregressive model abn Obtaining
The autoregressive model is as follows:
where α is an attenuation parameter indicating the propagation degree, th 2 Is a fluctuation threshold;
an outlier representing the motion interaction field at time t;
step three, utilizing the abnormal value of the motion interaction field obtained in the step two
Setting threshold th 3 The vehicle collision is predicted, and the expression is:
wherein L is t Is the final label of the t frame anomaly. If it is
Judging that collision occurs, and carrying out early warning; otherwise, it is determined that the vehicle is not involved in a collision.
2. The rapid and high-precision vehicle collision prediction method according to claim 1, characterized in that: in the second and third steps, the detection precision is highest when the attenuation parameter alpha is 0.8 through experimental analysis.
3. The rapid and high-precision vehicle collision prediction method according to claim 1, characterized in that: in the third step, the abnormal value obtained by the area difference is determined, and the lower the set early warning threshold value is, the more sensitive to the vehicle collision prediction is, but the accuracy of the collision prediction is reduced, and the abnormal value is set to be about 1/3 of the collision threshold value in the experiment.
The invention has the remarkable effects that: the method has the advantages of small calculated amount, high calculated speed, strong noise immunity and high detection precision, and can be used for carrying out real-time vehicle collision prediction and detection on the vehicle collision phenomenon in the video with higher accuracy.
Drawings
FIG. 1 is a flow chart of a vehicle collision prediction algorithm;
FIG. 2 is a video frame and its motion interaction field of a toy car simulating normal traffic;
FIG. 3 is a video frame and its motion interaction field of a toy car simulating abnormal traffic;
fig. 4 is an outlier variation of a video motion interaction field containing a collision frame.
Detailed Description
The invention will be described in further detail with reference to the drawings and the specific examples.
Fig. 1 is a flowchart of an algorithm according to the present invention.
The parameters in the algorithm are set as follows:
(A) M N is typically 240X 320;
(B)α＝0.8；
(C)th 1 ＝55；
(D)th 2 ＝300；
(E)th 3 ＝1100；
(F)σ＝10；
a video is input, the left side of fig. 2 is a frame which is taken from a car simulated traffic accident video and is not collided with, and the left side of fig. 3 is a frame which is taken from the same video and is likely to collide with the vehicle. Downsampling an image frame in the video results in an image frame f (x, y) having an mxn value of 240×320. Then the algorithm obtains motion information by using a Farnesback optical flow method: the velocity in the x-direction and the velocity in the y-direction, thereby obtaining an optical flow field V of 240X 320 0x And V 0y The method comprises the steps of carrying out a first treatment on the surface of the Downsampling the optical flow field results in optical flow fields Vx and Vy of size 24 x 32.
Using the resulting Vx and Vy, the kernel function is carried in:
by passing through
Obtaining a motion interaction field F with the size of 24 multiplied by 32 1 (x, y), then to F 1 (x, y) Using bicubic interpolation to obtain a largeAs small as 2M x 2N motion interaction field F 2 (x, y). As shown in the right side of fig. 2 and the right side of fig. 3, the motion interaction field of a frame in which the vehicle is not yet crashed and the motion interaction field of a frame in which the vehicle is likely to crash are taken from the same video.
Using the determined motion interaction fields F respectively 2 The absolute value of the pixel value in (x, y) is greater than th 1 Sum S of positive and negative area 1 、S 2 By M abn ＝|S 1 -S 2 Computing outlier M abn . Then smoothing M using an auto-regressive model with attenuation parameter α=0.8 abn Obtaining abnormal values of two frames of video
Wherein the method comprises the steps of
At this time by an abnormal value
A vehicle collision is predicted.
FIG. 4 is an outlier per frame of a selected video segment, it being seen that the outlier of the uncorrupted frames is less than the threshold th 3 =1100, whereas the collision frame is larger than it.
As can be seen from fig. 4: with the pre-warning threshold 300 set for a possible collision, wherein frame 176 is the collision frame, and frame 172 and before is the normal frame. The threshold is designed, and 4 frames of predictions are advanced to collide.
Claims (3)
1. A rapid and high-precision vehicle collision prediction method is characterized by comprising the following steps:
step one, constructing a motion interaction field F 2 (x, y) according to the following steps:
firstly, inputting a video frame, and performing downsampling or upsampling on an original image frame to obtain an image frame f (x, y) with the size of MxN;
secondly, extracting the motion information of a target object in the video sequence by using a farnebback optical flow algorithm: processing the ith pixel value f (x i ,y i ) Obtaining the velocity in the x direction
And speed in y direction>
Thereby obtaining an optical flow field V with the size of MxN 0x And V 0y ；
Thirdly, downsampling an optical flow field to obtain optical flow fields Vx and Vy with the size of M/10 XN/10;
fourth, vx and Vy are applied to kernel function K to obtain motion interaction field F with the size of M/10 XN/10 1 (x, y) having the expression:
wherein K is a kernel function, and the expression is:
in the above-mentioned method, the step of,
is forward center position +.>
For the rearward center position, k is the width sigma and the center is (x c ,y c ) The expression of the Gaussian kernel function is as follows:
fifth step, for F 1 (x, y) obtaining a motion interaction field F with the size of 2 Mx 2N by bicubic interpolation 2 (x,y)；
Step two, obtaining the abnormal value of the motion interaction field at the time t by using the area difference method
The method comprises the following steps:
first, utilize threshold th 1 The motion interaction field F obtained in the last step is obtained 2 The absolute value of the pixel value in (x, y) is greater than th 1 The sum of the positive area of (2) is S 1 The sum of the areas of the negative areas is S 2 ；
Second, calculating the absolute value M of the positive and negative area difference abn The expression is:
M abn ＝|S 1 -S 2 |
third step, smoothing M using autoregressive model abn Obtaining
The autoregressive model is as follows:
where α is an attenuation parameter indicating the propagation degree, th 2 For the fluctuation threshold value,
an outlier representing the motion interaction field at time t;
step three, utilizing the abnormal value of the motion interaction field obtained in the step two
Setting threshold th 3 The vehicle collision is predicted, and the expression is:
wherein L is t Is the final label of t frame abnormality; if it is
Judging that collision occurs, and carrying out early warning; otherwise, it is determined that the vehicle is not involved in a collision.
2. The rapid and high-precision vehicle collision prediction method according to claim 1, characterized in that: in the second and third steps, the detection precision is highest when the attenuation parameter alpha is 0.8 through experimental analysis.
3. The rapid and high-precision vehicle collision prediction method according to claim 1, characterized in that: in the third step, the abnormal value obtained by the area difference is judged, and the lower the set early warning threshold value is, the more sensitive to the vehicle collision prediction is, but the accuracy of the collision prediction is reduced, and the lower the set early warning threshold value is, in the experiment, 1/3 of the collision threshold value is set.
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