CN111383462A - New traffic accident scene video speed measurement method based on four-point plane homography - Google Patents

New traffic accident scene video speed measurement method based on four-point plane homography Download PDF

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CN111383462A
CN111383462A CN202010197976.8A CN202010197976A CN111383462A CN 111383462 A CN111383462 A CN 111383462A CN 202010197976 A CN202010197976 A CN 202010197976A CN 111383462 A CN111383462 A CN 111383462A
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point
video
formula
vehicle
points
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陈强
刘晓锋
关志伟
彭涛
候海晶
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Tianjin University of Technology and Education China Vocational Training Instructor Training Center
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data

Abstract

The invention discloses a new traffic accident scene video speed measurement method based on four-point plane homography, which is characterized by comprising the following steps of: the method comprises the following steps: checking the video of the accident scene to ensure that the video is intact and is an original video, and performing a second step: checking and recording the frame rate of the field video recording, extracting all key frame photos in the video recording by using video processing software, and performing the third step: at the space plane n1Digital image plane pi of sum camera2Respectively select 4 pairs of object points Pk(xk,yk) And corresponding image point Ik(uk,vk) And step four: for the object point Pk(xk,yk) And said image point Ik(uk,vk) Carrying out normalization processing, wherein the coordinate of the processed point is PCk(xCk,yCk) And point ICk(uCk,vCk) And step five: using the formula (1), solving a vector H by using the coordinates after the normalization processing, constructing a projective transformation matrix H by using the vector H, and simultaneously calculating the matrix H‑1. The invention relates to the field of detection equipment, in particular to a new traffic accident scene video speed measurement method based on four-point plane homography. The invention facilitates determination of vehicle speed.

Description

New traffic accident scene video speed measurement method based on four-point plane homography
Technical Field
The invention relates to the field of detection equipment, in particular to a new traffic accident scene video speed measurement method based on four-point plane homography.
Background
Among all kinds of fatal traffic accidents, speeding is a key problem. How to judge the speed of the vehicles participating in the accident after the accident happens brings difficulty to law enforcement officers in investigating the occurrence process of the traffic accident and obtaining evidence on site, and consumes a great deal of time. Therefore, it is necessary to calculate and analyze the vehicle speed of the participating vehicle at the time of the accident.
According to the traditional vehicle speed estimation method, after relevant distances such as a vehicle stopping position, a brake dragging mark, a tire mark and the like are measured on site by adopting a measuring tool, the vehicle speed under a typical collision working condition is calculated according to an energy conservation and momentum conservation empirical formula. The motion parameter calculation of the vehicle in the motion states of translation, sideslip/sideslip, side-tipping/side-turning, pitching and the like is completed by means of an empirical calculation model, and the analysis of the acting force between the wheels and the ground is completed by means of a tire ground mechanics calculation model. Finally, the vehicle speed is calculated using simulation reproduction software to measure and calculate the determined parameters.
With the forced spread of vehicle airbags, the vehicle speed at the time of an accident can also be determined using an emergency recording function provided in an event data recorder in an airbag control unit.
At present, the construction of video monitoring systems has been widely applied to the prevention of criminal behaviors, and road traffic management video monitoring networks are gradually formed. More and more road traffic accident sites can acquire monitoring video data, and visual fact basis is provided for traffic accident handling. Meanwhile, the installation of the automobile driving recorder is more and more popular, and the purpose of the automobile driving recorder is to serve as visual evidence for identifying the responsibility of a driver when the driver is in danger in the driving process. Due to the reasons, the demand of directly calculating the speed of the participating vehicle by relying on the accident scene video is more and more urgent, and a convenient and reliable method for directly calculating the speed of the participating vehicle in the accident scene video is urgently needed.
The vehicle speed is determined from the video recording data at the time of the occurrence of the accident. One method is based on the characteristics that the front and the rear vehicle lamp regions of the vehicle are bilaterally symmetrical, have larger brightness compared with other regions and are basically consistent with gray level change, the vehicle lamps are selected as feature blocks to increase the positioning precision when the vehicle is positioned and tracked, and the vehicle is accurately positioned to the vehicle lamp regions in a short time by utilizing a gray level difference horizontal superposition projection method to realize the positioning and tracking of the vehicle. One method is to adopt Harris detection operator for detecting motion corner points as an algorithm for extracting vehicle characteristics, calibrate camera parameters by an approximate conversion method, and utilize a gray level correlation function as a characteristic matching function to realize the measurement of the movement distance of the vehicle in a certain frame difference time. One method is to apply the cross-ratio theory in photogrammetry to video analysis, synthesize the start frame and the end frame of the video, and directly estimate the driving speed of the vehicle. The other method is a virtual reality method based on a computer, and the vehicle speed in the traffic accident scene is estimated by performing three-dimensional reconstruction on the traffic accident scene. Another method is to estimate the speed of the participating vehicle from the video image after deriving the probability interval and the confidence interval using the monte carlo method.
The above-mentioned domestic and foreign experts and scholars have the following disadvantages for a great deal of research and practice on the vehicle speed calculation method in traffic accidents.
(1) After the measuring tool is used for measuring relevant information on site, the vehicle speed under a typical collision working condition is calculated according to an empirical formula, and the defects that the time is long, the survey accuracy is low and the vehicle speed cannot be calculated under an atypical working condition exist.
(2) The vehicle speed at the time of the accident is determined using an emergency recording function provided in an event data recorder in an airbag control unit. When some accidents happen, the event data recorder does not record corresponding key information such as collision speed, acceleration and the like because the detonation condition of the safety air bag is not achieved.
(3) The vehicle speed is determined from the video recording data at the time of the occurrence of the accident. There is a difficulty encountered in the method of estimating the speed of the vehicle using the cross ratio theory when the photographing direction of the camera is the same as the traveling direction of the vehicle.
Disclosure of Invention
The invention aims to provide a new traffic accident scene video speed measurement method based on four-point plane homography, which is convenient for determining the speed of a vehicle.
The invention adopts the following technical scheme to realize the purpose of the invention:
a new traffic accident scene video speed measurement method based on four-point plane homography is characterized by comprising the following steps:
the method comprises the following steps: checking the video of the accident scene to ensure that the video is intact and is an original video;
step two: checking and recording the frame rate of a field video recording, and extracting all key frame photos in the video recording by using video processing software;
step three: at the space plane n1Digital image plane pi of sum camera2Respectively select 4 pairs of object points Pk(xk,yk) And corresponding image point Ik(uk,vk)；
Step four: for the object point Pk(xk,yk) And said image point Ik(uk,vk) Carrying out normalization processing, wherein the coordinate of the processed point is PCk(xCk,yCk) And point ICk(uCk,vCk)；
Step five: using the formula (1), solving a vector H by using the coordinates after the normalization processing, constructing a projective transformation matrix H by using the vector H, and simultaneously calculating the matrix H-1；
Kh＝b (1)
Wherein: vector h ═ h11,h12,h13,h21,h22,h23,h31,h32)T；
Vector b ═ u1,u2,u3,u4,v1,v2,v3,v4)T；
Matrix array
Figure BDA0002418306040000031
Step six: using the formula (2), from the matrix H-1Calculating the digital image plane n of the camera2Image point I onC(uC,vC) Corresponding object point PC(xC,yC)；
Figure BDA0002418306040000032
Wherein: the matrix H is called a projective transformation matrix;
step seven: for the object point P calculated in the step sixC(xC,yC) Performing reduction calculation, recovering an object point P (x, y), and calculating and outputting a corrected image;
step eight: measuring and recording an actual distance corresponding to the corrected image by extracting feature point information on the vehicle;
step nine: superposing the corrected images together to create a composite image, and extracting an independent track image;
step ten: calculating the running distance of the vehicle between any two points;
step eleven: calculating the vehicle speed by using the formula (3) and establishing a speed curve of the vehicle running track;
Figure BDA0002418306040000041
wherein: v is the calculated speed of the vehicle in the video;
l is the distance traveled by the vehicle feature points between the two video key frames;
t is the interval time of two video key frames, and can be calculated by a video frame rate.
As a further limitation of this embodiment, any three spatial points of the four plane points in the third step are not collinear.
As a further limitation of the present technical solution, the fourth step further includes the following steps:
step four, firstly: translation transformation, namely constructing a coordinate system by taking the centers of all points as the original points, and converting the coordinates of each point into coordinates under the coordinate system by using a formula (4);
Figure BDA0002418306040000042
wherein:
Figure BDA0002418306040000043
step four and step two: scaling transformation, adopting isotropic scaling to maintain coordinate transformation consistency of coordinate points, making average distance between each point and the translation transformation coordinate origin equal to
Figure BDA0002418306040000044
That is, the radius of the circle formed by the 4 points is 1, and the scaling factor CP、CICan be calculated by equation (5);
Figure BDA0002418306040000045
the coordinates of each point after the scaling transformation are as follows:
Figure BDA0002418306040000051
as a further limitation of the present invention, the seventh step is to use the formula (7) to align the object point P with the formula (4), the formula (5) and the formula (6)C(xC,yC) And (5) performing reduction calculation.
Figure BDA0002418306040000052
Wherein:
Figure BDA0002418306040000053
the same as defined in formula (4); cPThe same as defined in formula (5).
Compared with the prior art, the invention has the advantages and positive effects that:
the invention provides a new traffic accident scene video speed measurement method based on four-point plane homography, which directly determines the driving speed of a vehicle from a traffic accident investigation video, creates a driving track curve, analyzes the braking deceleration of the participated vehicle and the operation process of a driver, can solve the difficulty of video calculation when the shooting direction of a camera is the same as the driving direction of the vehicle, and does not need to know the self geometric dimension of the accident participated vehicle in the video.
Drawings
Fig. 1 is a flow chart of a new traffic accident scene video speed measurement method based on four-point plane homography.
FIG. 2 illustrates the frame rate, key frame and field coordinate definitions of the surveillance video of the present invention.
Fig. 3 is a schematic diagram of the video recorder imaging of the present invention.
FIG. 4 is a schematic diagram of the normalization process of the present invention.
Fig. 5 is an image of the vehicle travel track point of the present invention.
Fig. 6 is a vehicle travel track speed curve of the present invention.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the embodiment.
As shown in fig. 1 to 6, the present invention includes the following steps:
the method comprises the following steps: checking the video of the accident scene to ensure that the video is intact and is an original video;
step two: checking and recording the frame rate of a field video recording, and extracting all key frame photos in the video recording by using video processing software;
step three: at the space plane n1Digital image plane pi of sum camera2Respectively select 4 pairs of object points Pk(xk,yk) And corresponding image point Ik(uk,vk)；
Step four: for the object point Pk(xk,yk) And said image point Ik(uk,vk) Carrying out normalization processing, wherein the coordinate of the processed point is PCk(xCk,yCk) And point ICk(uCk,vCk)；
Step five: using the formula (1), solving a vector H by using the coordinates after the normalization processing, constructing a projective transformation matrix H by using the vector H, and simultaneously calculating the matrix H-1；
Kh＝b (1)
Wherein: vector h ═ h11,h12,h13,h21,h22,h23,h31,h32)T；
Vector b ═ u1,u2,u3,u4,v1,v2,v3,v4)T；
Matrix array
Figure BDA0002418306040000061
Step six: using the formula (2), from the matrix H-1Calculating the digital image plane n of the camera2Image point I onC(uC,vC) Corresponding object point PC(xC,yC)；
Figure BDA0002418306040000062
Wherein: the matrix H is called a projective transformation matrix;
step seven: for the object point P calculated in the step sixC(xC,yC) Performing reduction calculation, recovering an object point P (x, y), and calculating and outputting a corrected image;
step eight: measuring and recording an actual distance corresponding to the corrected image by extracting feature point information on the vehicle;
step nine: superposing the corrected images together to create a composite image, and extracting an independent track image;
step ten: calculating the running distance of the vehicle between any two points;
step eleven: calculating the vehicle speed by using the formula (3) and establishing a speed curve of the vehicle running track;
Figure BDA0002418306040000071
wherein: v is the calculated speed of the vehicle in the video;
l is the distance traveled by the vehicle feature points between the two video key frames;
t is the interval time of two video key frames, and can be calculated by a video frame rate.
Any three spatial points of the four plane points in the third step are not collinear.
The fourth step further comprises the following steps:
step four, firstly: translation transformation, namely constructing a coordinate system by taking the centers of all points as the original points, and converting the coordinates of each point into coordinates under the coordinate system by using a formula (4);
Figure BDA0002418306040000072
wherein:
Figure BDA0002418306040000073
step four and step two: scaling transformation, adopting isotropic scaling to maintain coordinate transformation consistency of coordinate points, making average distance between each point and the translation transformation coordinate origin equal to
Figure BDA0002418306040000074
I.e. a circle of 4 pointsIs 1, scaling factor CP、CICan be calculated by equation (5);
Figure BDA0002418306040000075
the coordinates of each point after the scaling transformation are as follows:
Figure BDA0002418306040000076
the specific step of the seventh step is to use the formula (7) to align the object point P by the formula (4), the formula (5) and the formula (6)C(xC,yC) And (5) performing reduction calculation.
Figure BDA0002418306040000081
Wherein:
Figure BDA0002418306040000082
the same as defined in formula (4); cPThe same as defined in formula (5).
The working process of the invention is as follows: and checking the video of the accident scene to ensure that the video is intact and is the original video. And checking and recording the frame rate of the field video recording, and extracting all key frame photos in the video recording by using video processing software. At the space plane n1Digital image plane pi of sum camera2Respectively select 4 pairs of object points Pk(xk,yk) And corresponding image point Ik(uk,vk). For the object point Pk(xk,yk) And said image point Ik(uk,vk) Carrying out normalization processing, wherein the coordinate of the processed point is PCk(xCk,yCk) And point ICk(uCk,vCk). Solving a vector H by using the coordinates after the normalization processing, constructing a projective transformation matrix H by using the vector H, and simultaneously calculating the matrix H-1. By matrix H-1Calculating the digital image plane n of the camera2Image point I onC(uC,vC) Corresponding object point PC(xC,yC). Object point PC(xC,yC) And (5) performing reduction calculation, recovering the object point P (x, y), and calculating and outputting a corrected image. And measuring and recording the actual distance corresponding to the corrected image by extracting the characteristic point information on the vehicle. The corrected images are superimposed together to create a composite image, and a single track image is extracted. And calculating the running distance of the vehicle between any two points. And calculating the vehicle speed and establishing a speed curve of the vehicle running track.
The above disclosure is only for the specific embodiment of the present invention, but the present invention is not limited thereto, and any variations that can be made by those skilled in the art should fall within the scope of the present invention.

Claims (4)

1. A new traffic accident scene video speed measurement method based on four-point plane homography is characterized by comprising the following steps:
the method comprises the following steps: checking the video of the accident scene to ensure that the video is intact and is an original video;
step two: checking and recording the frame rate of a field video recording, and extracting all key frame photos in the video recording by using video processing software;
step three: at the space plane n1Digital image plane pi of sum camera2Respectively select 4 pairs of object points Pk(xk,yk) And corresponding image point Ik(uk,vk)；
Step four: for the object point Pk(xk,yk) And said image point Ik(uk,vk) Carrying out normalization processing, wherein the coordinate of the processed point is PCk(xCk,yCk) And point ICk(uCk,vCk)；
Step five: using the formula (1), solving a vector H by using the coordinates after the normalization processing, constructing a projective transformation matrix H by using the vector H, and simultaneously calculating the matrix H-1；
Kh＝b (1)
Wherein: vector h ═ h11,h12,h13,h21,h22,h23,h31,h32)T；
Vector b ═ u1,u2,u3,u4,v1,v2,v3,v4)T；
Matrix array
Figure FDA0002418306030000011
Step six: using the formula (2), from the matrix H-1Calculating the digital image plane n of the camera2Image point I onC(uC,vC) Corresponding object point PC(xC,yC)；
Figure FDA0002418306030000012
Wherein: the matrix H is called a projective transformation matrix;
step seven: for the object point P calculated in the step sixC(xC,yC) Performing reduction calculation, recovering an object point P (x, y), and calculating and outputting a corrected image;
step eight: measuring and recording an actual distance corresponding to the corrected image by extracting feature point information on the vehicle;
step nine: superposing the corrected images together to create a composite image, and extracting an independent track image;
step ten: calculating the running distance of the vehicle between any two points;
step eleven: calculating the vehicle speed by using the formula (3) and establishing a speed curve of the vehicle running track;
Figure FDA0002418306030000021
wherein: v is the calculated speed of the vehicle in the video;
l is the distance traveled by the vehicle feature points between the two video key frames;
t is the interval time of two video key frames, and can be calculated by a video frame rate.
2. The new traffic accident scene video speed measurement method based on the four-point plane homography according to claim 1, characterized in that: any three spatial points of the four plane points in the third step are not collinear.
3. The new traffic accident scene video speed measurement method based on the four-point plane homography according to claim 1, characterized in that: the fourth step further comprises the following steps:
step four, firstly: translation transformation, namely constructing a coordinate system by taking the centers of all points as the original points, and converting the coordinates of each point into coordinates under the coordinate system by using a formula (4);
Figure FDA0002418306030000022
wherein:
Figure FDA0002418306030000023
step four and step two: scaling transformation, adopting isotropic scaling to maintain coordinate transformation consistency of coordinate points, making average distance between each point and the translation transformation coordinate origin equal to
Figure FDA0002418306030000024
That is, the radius of the circle formed by the 4 points is 1, and the scaling factor CP、CICan be calculated by equation (5);
Figure FDA0002418306030000031
the coordinates of each point after the scaling transformation are as follows:
Figure FDA0002418306030000032
4. the new traffic accident scene video speed measurement method based on the four-point plane homography according to the claim 3, characterized in that: the specific step of the seventh step is to use the formula (7) to align the object point P by the formula (4), the formula (5) and the formula (6)C(xC,yC) And (5) performing reduction calculation.
Figure FDA0002418306030000033
Wherein:
Figure FDA0002418306030000034
the same as defined in formula (4); cPThe same as defined in formula (5).
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