CN108230393A - A kind of distance measuring method of intelligent vehicle forward vehicle - Google Patents
A kind of distance measuring method of intelligent vehicle forward vehicle Download PDFInfo
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- CN108230393A CN108230393A CN201611150164.8A CN201611150164A CN108230393A CN 108230393 A CN108230393 A CN 108230393A CN 201611150164 A CN201611150164 A CN 201611150164A CN 108230393 A CN108230393 A CN 108230393A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
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- G—PHYSICS
- 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
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Abstract
The invention discloses a kind of distance measuring methods of intelligent vehicle forward vehicle, include the following steps：S1：Road ahead video image is acquired, image is pre-processed, including image cutting-out, image gray processing, image filtering and image binaryzation；S2：Road image marginal information is strengthened using Canny boundary operators；S3：Lane line is detected using Hough transform method；S4：Vehicle bottom shade is split, the rectangle frame there may be vehicle, i.e. area-of-interest (RIO) is established, vehicle is accurately detected；S5：Visual projection's model is established, calculates the pixel coordinate value at image base midpoint and the pixel coordinate value at RIO bases midpoint；S6：Establish calibrating template, camera intrinsic parameter calibration；S7：Establish geometry ranging model；S8：It obtains the road plane coordinate system coordinate at image base midpoint and RIO bases midpoint, with reference to the calibration result of camera intrinsic parameter, the distance between this vehicle and front vehicles is being calculated according to distance calculation formula.
Description
Technical field
The present invention relates to a kind of distance measuring methods of intelligent vehicle forward vehicle.
Background technology
During intelligent vehicle running, since road environment is more complicated, it is desirable to allow its completely nobody independently travel, perhaps
Multiplexing also needs to us and carries out in-depth study.Currently based on machine vision intelligent vehicle obstacle detection technology and enclose
Other the relevant technologies around its expansion are the faster key technologies of development, this technology passes through the vision that is mounted on intelligent vehicle
Sensor obtains ambient condition information, vehicle front whether there are obstacles is carried out using the environmental information of acquisition real-time, accurate
Really detect, and can intelligent vehicle is directly related to the success or not of detection of obstacles smoothly hide obstacle and security row
It sails.With the continuous improvement of Hyundai Motor degree of intelligence, while intelligent vehicle automatic Pilot is striven for, intelligence also should ensure that
Can vehicle traffic safety, it is desirable to accomplish safety just drive must just monitor in real time this vehicle and forward vehicle it is opposite away from
From.
Visual token occupies important as one of basic technology in field of machine vision in intelligent vehicle-carried vision system
Status.In vision spacing, the foundation of visual projection's model, coordinate information (the i.e. target point that will there is computer to directly obtain
Pixel coordinate value) and real space in coordinate information (i.e. the world coordinates value of target point) between completely connect,
The extraction space three-dimensional information from two dimensional image is made to become reality.As long as accurately calculating the inside and outside parameter matrix of video camera,
Accurate position coordinates of the target point in world coordinates can be obtained according to vision mode, and then have range formula to calculate target
The distance between point, therefore the calculating of camera interior and exterior parameter matrix is very crucial, we are by the inside and outside parameter square of video camera
The calculating process of battle array is referred to as the calibration of video camera.
Be exactly at present corresponding points standardization using widest scaling method, in this method, the intrinsic parameter of video camera be by
What the interior geometry and optical signature of video camera determined, be changeless, outer parameter then determines the image of video camera
Plane, once video camera moves, needs to re-start it calibration relative to the three-dimensional position of objective world coordinate system.
Therefore, corresponding points standardization is suitable for situation when camera position is fixed.And the video camera for being mounted on intelligent vehicle comes
It says, outer parameter constantly changes with the traveling of vehicle, and these variations are unforeseen, so according to corresponding points
Calibration result is limited by very large to carry out range measurement.
Invention content
The technical problem to be solved in the present invention is to provide a kind of distance measuring methods of intelligent vehicle forward vehicle.
A kind of distance measuring method of intelligent vehicle forward vehicle, includes the following steps：
S1：Road ahead video image is acquired, image is pre-processed, including image cutting-out, image gray processing, image
Filtering and image binaryzation；
S2：Road image marginal information is strengthened using Canny boundary operators；
S3：Lane line is detected using Hough transform method；
S4：Vehicle bottom shade is split, establishes the rectangle frame there may be vehicle, i.e. area-of-interest (RIO),
Vehicle is accurately detected；
S5：Visual projection's model is established, the pixel coordinate value at image base midpoint is calculated and the pixel at RIO bases midpoint is sat
Scale value；
S6：Establish calibrating template, camera intrinsic parameter calibration；
S7：Establish geometry ranging model；
S8：The road plane coordinate system coordinate at image base midpoint and RIO bases midpoint is obtained, with reference to camera intrinsic parameter
Calibration result, the distance between this vehicle and front vehicles is being calculated according to distance calculation formula.
Further, which is characterized in that the method for visual projection's model foundation is specific as follows：
1) assume that coordinate values of any point P under camera coordinate system in space is (XC,YC,ZC), subpoint P '
Coordinate value on imaging plane is (x, y), and the focal length of video camera is f, represents that perspective projection relationship is as follows with matrix form：
2) assume the origin O in pixel coordinate system1Position in pixel coordinate system is denoted as (u0,v0), then it is sat in image
Any point (x, y) in mark system can be found in pixel coordinate system its for coordinate (u, v), the conversion between two coordinate systems
Relationship is represented by：
Wherein, dx and dy represents physics of the unit pixel on image coordinate plane x and y directions respectively
Size, dx=dy；
Matrix form 1) above formula is substituted into, can obtain：
Wherein,M1Represent video camera Intrinsic Matrix, parameter therein only with video camera in itself
Structure is related；
3) it is (X to assume coordinate values of the spatial point P under camera coordinate systemC,YC,ZC), this is in world coordinate system
Coordinate is (Xw,Yw,Zw), then the transformational relation between them is represented by：
Wherein, R be the orthogonal spin matrixs of 3*3, T be 3*1 translation matrix, 0=(0,0,0)T, M2Represent the outer ginseng of video camera
Matrix number, rotation, translation relation between expression camera coordinate system and world coordinate system；
4) it is as follows to establish complete visual projection model：
Further, the scaling method of the camera intrinsic parameter is as follows：
1) plane where assuming line segment AB is beta version plane, and plane where line segment ab is the plane of delineation, and line segment ab is line
The picture that section AB is shortened into, the length of line segment AB is l, and line segment ab length is l ', then：
2) dx and dy can directly be asked for according to the ratio of video camera photosensitive element size and image resolution ratio；According toAcquire fx,fy, thus the intrinsic parameter of video camera can all be decided.
Further, the geometry ranging model is as follows：
Wherein, position of the origin of image coordinate system in pixel coordinate system is denoted as (u0,v0), height of the video camera away from ground
It spends for h, the angle between optical axis and ground is α,Dx and dy represents unit pixel in image coordinate respectively
Physical size on plane x and y directions.
Further, the specific method for calculating distance between this vehicle and front vehicles is as follows：
1) assume that the distance between this vehicle and front vehicles for d, enable d=d1+d2, wherein d1For the nearest visual field of video camera with
The distance between this vehicle car body front end, d2For the distance between the nearest visual field of video camera and front vehicles；
2)d1Acquiring method：After camera height and pitch angle are fixed, vehicle mounted camera shooting road ahead is utilized
Image, location point that can be at the nearest visual field of positioning shooting machine by observing the image that takes, then to the location point to vehicle
The distance of body front end carries out practical measurement；
3)d2Acquiring method：
A) road ahead image is obtained by video camera；
B) area-of-interest for including front vehicles is determined by vehicle detecting algorithm, and is marked with box；
C) pixel coordinate value (u at the area-of-interest box base midpoint is asked for1,v1)；
D) pixel coordinate value (u at image base midpoint is asked for2,v2)；
E) according to geometry ranging model formation, the coordinate (u in the plane of delineation1,v1), (u2,v2) be converted into accordingly
Coordinate (X in the plane coordinate system of road1,Y1), (X2,Y2)；
F) d obtained by distance calculation formula2For：
The beneficial effects of the invention are as follows：
The present invention, using the geometrical relationship of perspective projection, derives front from the modeling principle of visual projection's model
The method of vehicle distance survey only needs the inner parameter intrinsic to video camera to demarcate in visual token, avoids to institute
There is the complex process that camera parameters are demarcated, as long as selecting suitable video camera pitch angle, it is possible to reach higher vehicle
Away from measurement accuracy, distance measuring method real-time of the present invention is high, and is also in tolerance interval to the relative error of telemeasurement
It is interior, it disclosure satisfy that the application requirement of distance survey on structured road.
Specific embodiment
The present invention is further elaborated for specific examples below, but not as a limitation of the invention.
It is identical to test 1 video camera pitch angle, the different Range finding experiments of camera height
The pitch angle α for setting video camera is 4.5 °, by changing height h of the video camera apart from ground, on straight road surface
It is upper that Image Acquisition is carried out to the front vehicles of 10,20,40,70,100,110,120 different distances respectively, then surveyed with the present invention
Front vehicles distance is carried out away from method to calculate, and is finally compared result of calculation and practical spacing, measurement result such as 1 institute of table
Show.
Range measurements during 1 video camera difference mounting height of table
Test that 2 camera heights are identical, the different Range finding experiments of video camera pitch angle
In the case where other parameter is constant, it is 1.25m that height of the fixed video camera apart from ground, which is h, is taken the photograph by changing
The size of camera pitch angle α, respectively to the front vehicle of 10,20,40,70,100,110,120 different distances on straight road surface
Carry out Image Acquisition, then with distance measuring method of the present invention carry out front vehicles apart from calculating, finally by result of calculation and reality
Spacing be compared, measurement result is as shown in table 2.
Range measurements during 2 video camera difference mounting height of table
It is compared and found by the experimental data in Tables 1 and 2, the feelings of mounting height variation constant in video camera pitch angle
Under condition, distance results obtained by calculation error very little compared with actual range；And video camera mounting height is constant, pitch angle
In the case of becoming larger, the result of calculation of the spacing error compared with actual range significantly increases, it is contemplated that the knots such as highway
Structure road is relatively flat, and camera height varies less caused by slight jolt in driving conditions, caused to miss
Difference can be ignored substantially, therefore, as long as selecting suitable video camera pitch angle, it is possible to reach higher distance survey precision.
3 distance measuring methods of the present invention are tested to test with other distance measuring method Comparative results
Distance measuring method A：Using the method based on gradient orientation histogram and linear SVM grader to front vehicle
It is detected, according to the testing result of vehicle, inside and outside the transformational relation and video camera between perspective transform, coordinate system
Parameter realizes the measurement of this vehicle and front vehicles distance.
Under identical camera parameters, distance measuring method of the present invention and distance measuring method A are subjected to Experimental Comparison, dismissed such as table
(setting h=1.25m, α=4.5 °) shown in 3.
3 two kinds of distance measuring method comparisons of table
Relative to distance measuring method A, distance measuring method real-time of the present invention is some higher, and the opposite of telemeasurement is missed
Difference is also in tolerance interval, disclosure satisfy that the application requirement of distance survey on structured road.
Claims (5)
1. a kind of distance measuring method of intelligent vehicle forward vehicle, which is characterized in that include the following steps：
S1：Road ahead video image is acquired, image is pre-processed, including image cutting-out, image gray processing, image filtering
And image binaryzation；
S2：Road image marginal information is strengthened using Canny boundary operators；
S3：Lane line is detected using Hough transform method；
S4：Vehicle bottom shade is split, the rectangle frame there may be vehicle, i.e. area-of-interest (RIO) are established, to vehicle
It is accurately detected；
S5：Visual projection's model is established, calculates the pixel coordinate value at image base midpoint and the pixel coordinate at RIO bases midpoint
Value；
S6：Establish calibrating template, camera intrinsic parameter calibration；
S7：Establish geometry ranging model；
S8：The road plane coordinate system coordinate at image base midpoint and RIO bases midpoint is obtained, with reference to the mark of camera intrinsic parameter
Determine as a result, calculating the distance between this vehicle and front vehicles according to distance calculation formula.
2. the distance measuring method of intelligent vehicle forward vehicle according to claim 1, which is characterized in that the visual projection
The method of model foundation is specific as follows：
1) assume that coordinate values of any point P under camera coordinate system in space is (XC,YC,ZC), subpoint P ' into
Coordinate value in image plane is (x, y), and the focal length of video camera is f, represents that perspective projection relationship is as follows with matrix form：
2) assume the origin O in pixel coordinate system1Position in pixel coordinate system is denoted as (u0,v0), then in image coordinate system
In any point (x, y) can be found in pixel coordinate system its for coordinate (u, v), the transformational relation between two coordinate systems
It is represented by：
Wherein, dx and dy represents physical size of the unit pixel on image coordinate plane x and y directions respectively,
Dx=dy；
Matrix form 1) above formula is substituted into, can obtain：
Wherein,M1Represent the Intrinsic Matrix of video camera, parameter therein and this body structure of video camera
It is related；
3) it is (X to assume coordinate values of the spatial point P under camera coordinate systemC,YC,ZC), coordinate of this in world coordinate system
For (Xw,Yw,Zw), then the transformational relation between them is represented by：
Wherein, R be the orthogonal spin matrixs of 3*3, T be 3*1 translation matrix, 0=(0,0,0)T, M2Represent the outer parameter square of video camera
Battle array, rotation, translation relation between expression camera coordinate system and world coordinate system；
4) it is as follows to establish complete visual projection model：
3. the distance measuring method of intelligent vehicle forward vehicle according to claim 1, which is characterized in that in the video camera
The scaling method of parameter is as follows：
1) plane where assuming line segment AB is beta version plane, and plane where line segment ab is the plane of delineation, and line segment ab is line segment AB
The picture shortened into, the length of line segment AB is l, and line segment ab length is l ', then：
2) dx and dy can directly be asked for according to the ratio of video camera photosensitive element size and image resolution ratio；According toAcquire fx,fy, thus the intrinsic parameter of video camera can all be decided.
4. the distance measuring method of intelligent vehicle forward vehicle according to claim 1, which is characterized in that the geometry ranging
Model is as follows：
Wherein, position of the origin of image coordinate system in pixel coordinate system is denoted as (u0,v0), height of the video camera away from ground is
H, the angle between optical axis and ground are α,Dx and dy represents unit pixel in image coordinate plane respectively
Physical size on x and y directions.
5. the distance measuring method of intelligent vehicle forward vehicle according to claim 1, which is characterized in that calculate this vehicle with before
The specific method of distance is as follows between square vehicle：
1) assume that the distance between this vehicle and front vehicles for d, enable d=d1+d2, wherein d1For the nearest visual field of video camera and this vehicle
The distance between car body front end, d2For the distance between the nearest visual field of video camera and front vehicles；
2)d1Acquiring method：After camera height and pitch angle are fixed, using vehicle mounted camera shooting road ahead image,
Location point that can be at the nearest visual field of positioning shooting machine by observing the image that takes, then to the location point to car body front end
Distance carry out practical measurement；
3)d2Acquiring method：
A) road ahead image is obtained by video camera；
B) area-of-interest for including front vehicles is determined by vehicle detecting algorithm, and is marked with box；
C) pixel coordinate value (u at the area-of-interest box base midpoint is asked for1,v1)；
D) pixel coordinate value (u at image base midpoint is asked for2,v2)；
E) according to geometry ranging model formation, the coordinate (u in the plane of delineation1,v1), (u2,v2) corresponding be converted into road and put down
Coordinate (X in areal coordinate system1,Y1), (X2,Y2)；
F) d obtained by distance calculation formula2For：
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| CN111998780B (en) * | 2019-05-27 | 2022-07-01 | 杭州海康威视数字技术股份有限公司 | Target ranging method, device and system |
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| CN110363819A (en) * | 2019-06-25 | 2019-10-22 | 华为技术有限公司 | The method and relevant device that image capture device is demarcated in intelligent automobile |
| CN110363819B (en) * | 2019-06-25 | 2023-03-03 | 华为技术有限公司 | Method for calibrating image acquisition equipment in intelligent automobile and related equipment |
| CN110717445A (en) * | 2019-10-09 | 2020-01-21 | 清华大学 | Front vehicle distance tracking system and method for automatic driving |
| CN110717445B (en) * | 2019-10-09 | 2022-08-23 | 清华大学 | Front vehicle distance tracking system and method for automatic driving |
| CN110991264A (en) * | 2019-11-12 | 2020-04-10 | 浙江鸿泉车联网有限公司 | Front vehicle detection method and device |
| CN111623776A (en) * | 2020-06-08 | 2020-09-04 | 昆山星际舟智能科技有限公司 | Method for measuring distance of target by using near infrared vision sensor and gyroscope |
| CN111996883A (en) * | 2020-08-28 | 2020-11-27 | 四川长虹电器股份有限公司 | Method for detecting width of road surface |
| CN112184806A (en) * | 2020-09-14 | 2021-01-05 | 国家电网有限公司 | Space distance measurement method based on three-dimensional live-action transformer substation |
| CN112215306A (en) * | 2020-11-18 | 2021-01-12 | 同济大学 | Target detection method based on fusion of monocular vision and millimeter wave radar |
| CN112215306B (en) * | 2020-11-18 | 2023-03-31 | 同济大学 | Target detection method based on fusion of monocular vision and millimeter wave radar |
| CN113221739A (en) * | 2021-05-12 | 2021-08-06 | 中国科学技术大学 | Monocular vision-based vehicle distance measuring method |
| CN113191303A (en) * | 2021-05-14 | 2021-07-30 | 山东新一代信息产业技术研究院有限公司 | Method for calculating front vehicle distance and steering based on single camera |
| CN113191303B (en) * | 2021-05-14 | 2023-04-18 | 山东新一代信息产业技术研究院有限公司 | Method for calculating distance and steering of front vehicle based on single camera |
| CN114200838A (en) * | 2021-12-08 | 2022-03-18 | 青岛中鸿重型机械有限公司 | Control method of intelligent electric scraper |
| CN114659527A (en) * | 2022-03-30 | 2022-06-24 | 北京理工大学 | Lane line optical ranging method based on inertia measurement unit compensation |
| CN117516485A (en) * | 2024-01-04 | 2024-02-06 | 东北大学 | Pose vision measurement method for automatic guiding and mounting of aircraft engine |
| CN117516485B (en) * | 2024-01-04 | 2024-03-22 | 东北大学 | Pose vision measurement method for automatic guiding and mounting of aircraft engine |
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