CN110307791B - Vehicle length and speed calculation method based on three-dimensional vehicle boundary frame - Google Patents
Vehicle length and speed calculation method based on three-dimensional vehicle boundary frame Download PDFInfo
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- G—PHYSICS
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/04—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness specially adapted for measuring length or width of objects while moving
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- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/18—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance
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Abstract
The invention provides a vehicle length and speed calculation method based on a three-dimensional vehicle boundary frame. The method comprises the following steps: (1) generating a vehicle Mask based on a Mask R-CNN network; (2) drawing tangents from three vanishing points in the scene to the generated vehicle mask to construct a three-dimensional vehicle boundary frame; (3) establishing a vehicle virtual detection area, and judging whether a vehicle is in the detection area according to whether the midpoint of the front edge of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area; (4) determining pixel coordinates of a road reference point by using a lane virtual line segment and a scene vanishing point, and solving a homography matrix between a road plane world coordinate and a corresponding pixel coordinate according to the known length of the lane virtual line segment and the known lane width; (5) calculating the actual length of the vehicle by using the homography matrix and the three-dimensional vehicle boundary frame; (6) and calculating the vehicle speed by using the homography matrix, the three-dimensional vehicle boundary frame and the virtual detection area. The invention has high calculation precision and low equipment cost, and can be effectively applied to an intelligent traffic system.
Description
Technical Field
The invention relates to a vehicle length and speed calculation method based on a three-dimensional vehicle boundary frame, and belongs to the fields of computer vision technology and intelligent traffic.
Background
Vehicle length and speed are important parameters in traffic vehicle information. Vehicle speed is usually obtained by sensors buried under the road surface or by placing a radar on the road. However, in this way, the cost of the equipment is high, and when the distance between the vehicles is very close, a large detection error is brought, and even the detection fails. Video technology is widely researched as a low-cost detection technology at present, but the detection precision is still not ideal. The length of the vehicle is an important index for classifying the vehicle type, and an effective and low-cost calculation method is still insufficient at present. How to effectively, highly accurately and inexpensively calculate the length and the speed of the vehicle is a difficult problem in the current traffic field.
Disclosure of Invention
In order to solve the existing problems, the invention discloses a vehicle length and speed calculation method based on a three-dimensional vehicle boundary frame, which is high in calculation precision and low in equipment cost.
The above purpose is realized by the following technical scheme:
a vehicle length and speed calculation method based on a three-dimensional vehicle boundary frame comprises the following steps:
(1) generating a vehicle Mask based on a Mask R-CNN network;
(2) drawing tangents from three vanishing points in the scene to the generated vehicle mask to construct a three-dimensional vehicle boundary frame;
(3) establishing a vehicle virtual detection area, and judging whether a vehicle is in the detection area according to whether the midpoint of the front edge of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area;
(4) calibrating the road surface to obtain a homography matrix between the world coordinates of the road plane and the corresponding pixel coordinates;
(5) calculating the actual length of the vehicle by using the homography matrix and the three-dimensional vehicle boundary frame;
(6) and calculating the vehicle speed by using the homography matrix, the three-dimensional vehicle boundary frame and the virtual detection area.
The method for calculating the length and the speed of the vehicle based on the three-dimensional vehicle boundary frame comprises the steps of (1) constructing the three-dimensional vehicle boundary frame, and specifically determining a first orthogonal vanishing point, a second orthogonal vanishing point and a third orthogonal vanishing point of a scene according to a lane line, a vehicle texture and a street lamp position in a traffic scene, and constructing the three-dimensional vehicle boundary frame by cutting a vehicle Mask generated by Mask R-CNN based on the three orthogonal vanishing points.
The method for calculating the length and the speed of the vehicle based on the three-dimensional vehicle boundary frame comprises the steps of (1) establishing a vehicle virtual detection area in a lens visual field range, and judging whether the vehicle is in the detection area according to whether the midpoint of the front edge of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area.
The vehicle length and speed calculation method based on the three-dimensional vehicle boundary frame is characterized in that the road surface is calibrated to further obtain a homography matrix H between the road plane world coordinate and the corresponding pixel coordinate,
firstly, determining pixel coordinates of lane virtual line segment endpoints, establishing straight lines according to the lane virtual line segment endpoints and a second scene vanishing point, and then obtaining pixel coordinates of intersection points of the straight lines and solid lines on two sides of a lane; taking the points with the known pixel coordinates as road surface calibration reference points; the world coordinates of the reference points can be obtained by using the known actual length and lane width of the lane dotted line section; the pixel coordinates of the reference point and the world coordinates are taken into the formula (2), and eight independent parameters in the homography matrix can be obtained by utilizing a least square method or singular value decomposition, so that a basis is provided for the calculation of the length and the speed of the vehicle based on the three-dimensional vehicle boundary frame.
In the formula (x)i,yi) And (X)i,Yi) Respectively representing the world coordinate and the pixel coordinate of the reference point i, and m is an independent parameter in the homography matrix H.
In the vehicle length calculation in the step (5), the mid-point world coordinates of the front and rear sides of the bottom surface of the three-dimensional vehicle boundary frame are calculated according to a homography matrix obtained by road surface calibration, and the difference value of the two world coordinates is the actual length of the vehicle.
In the vehicle speed calculation described in (6), the vehicle speed is calculated by using the distance and time that the vehicle travels in the virtual detection area, where the travel distance is calculated according to the world coordinate difference between the midpoints of the front edges of the bottom surfaces of the three-dimensional vehicle boundary frames corresponding to the entering and leaving detection areas of the vehicle, and the travel time is determined according to the video frame rate, and the vehicle speed calculation formula is as follows:
in the formula: v is the vehicle speed, L and T are the driving distance and time of the vehicle in the virtual detection area, NfIs the total number of frames, F, corresponding to the running process of the vehicle in the detection arearIs the frame rate of the video and,
and
respectively, the world coordinates of the middle point of the front side of the bottom surface of the three-dimensional bounding box corresponding to the vehicle entering and leaving detection areas.
Compared with the prior art, the invention has the beneficial effects that:
(1) compared with a calculation method utilizing three orthogonal vanishing points and lens height, the road plane calibration method provided by the invention has higher precision.
(2) Compared with the traditional speed measurement scheme based on the embedded sensor or the radar, the speed measurement method has the advantage that the required equipment cost is greatly reduced.
(3) The invention provides a monocular camera-based vehicle length calculation method, and provides a new mode for traffic vehicle classification.
Drawings
FIG. 1 is a construction of a three-dimensional vehicle bounding box;
FIG. 2 is a diagram of a road surface calibration reference point position and a verification target;
FIG. 3 is a schematic view of the vehicle length calculation;
fig. 4 is a vehicle speed calculation diagram.
Detailed Description
The present invention will be further described with reference to the following embodiments.
Taking a traffic scene on a certain bridge floor as an example, the length and the speed of a passing vehicle in a lens are detected. The method specifically comprises the following steps:
1. the method comprises the steps of respectively determining a first vanishing point, a second vanishing point and a third vanishing point of a scene according to a lane line, a vehicle texture and a street lamp position in a traffic scene, and determining an intersection point, namely a vanishing point, of different straight lines in the same direction in the scene according to a least square method. And based on the three orthogonal vanishing points, tangent lines are made to the vehicle Mask generated by Mask R-CNN to construct a three-dimensional vehicle boundary frame, as shown in FIG. 1, the angular points 1,2,3 and 4 of the three-dimensional boundary frame can be directly determined according to intersection points between the tangent lines, and then the other four angular points 5,6,7 and 8 of the three-dimensional boundary frame can be determined by utilizing the four angular points. And after all the eight corner points are determined, a three-dimensional vehicle boundary frame can be constructed.
2. In order to calculate the vehicle speed, a virtual vehicle detection area is established in the lens visual field range, and whether the vehicle is in the detection area is judged by whether the middle point of the front side of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area.
3. Since the homography matrix in equation (1) has 8 independent unknown parameters, at least 4 reference points that are not on a straight line are required to solve the homography matrix H. Firstly, determining pixel coordinates of the endpoint of the virtual line segment of the lane, then establishing straight lines according to the endpoint of the virtual line segment of the lane and the second vanishing point, and then obtaining the pixel coordinates of the intersection points of the straight lines and the solid lines on the two sides of the lane. These points of known pixel coordinates are taken as calibration reference points, as shown in fig. 2. The world coordinates of these reference points can be obtained using the known actual length of the dashed line segment of the lane and the lane width. And (3) bringing the pixel coordinates of the reference point and the world coordinates into the formula (2), and solving the homography matrix H by a least square method or singular value decomposition. In order to verify the reliability of the calibration method provided by the present invention, 15 dotted lane line segments of known length are used as the verification target to verify the calibration result of the present invention, and the verification target is shown in fig. 2. The lengths of the verification targets calculated from the three orthogonal vanishing points and the camera height and the lengths of the verification targets calculated according to the method of the present patent are listed in table 1. It can be seen from the table that the calculation errors of the method provided by the patent are less than 5%, which greatly exceeds the traditional method.
4. Inverting the obtained homography matrix to obtain H-1Then multiplying H by the homogeneous form of the coordinates of the midpoint pixels of the front and the rear sides of the bottom surface of the three-dimensional vehicle bounding box-1And obtaining corresponding homogeneous world coordinates, normalizing the homogeneous world coordinates by using a third component to obtain corresponding two-dimensional world coordinates, wherein the distance between the two-dimensional world coordinates of the middle points of the front side and the rear side of the bottom surface of the vehicle boundary frame is the actual length of the vehicle, and a vehicle length calculation schematic diagram is shown in fig. 3.
5. The vehicle speed is calculated by using the distance traveled by the vehicle in the virtual detection area and the time, as shown in fig. 4, wherein the travel distance is calculated by using the world coordinate difference between the middle points of the front sides of the bottom surfaces of the three-dimensional vehicle bounding boxes corresponding to the vehicle entering and leaving the detection area, and the travel time is determined according to the video frame rate, and the vehicle speed is calculated according to the following formula (3):
in conclusion, the vehicle length and speed calculation method based on the three-dimensional vehicle boundary frame provided by the invention can be effectively applied to an intelligent traffic system.
TABLE 1 calculation of verified target lengths on pavement
Claims (6)
1. A vehicle length and speed calculation method based on a three-dimensional vehicle boundary frame is characterized by comprising the following steps:
(1) generating a vehicle Mask based on a Mask R-CNN network;
(2) drawing tangents from three vanishing points in the scene to the generated vehicle mask to construct a three-dimensional vehicle boundary frame;
(3) establishing a vehicle virtual detection area, and judging whether a vehicle is in the detection area according to whether the midpoint of the front edge of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area;
(4) calibrating the road surface to obtain a homography matrix between the world coordinates of the road plane and the corresponding pixel coordinates;
(5) calculating the actual length of the vehicle by using the homography matrix and the three-dimensional vehicle boundary frame;
(6) and calculating the vehicle speed by using the homography matrix, the three-dimensional vehicle boundary frame and the virtual detection area.
2. The method of claim 1, wherein the method comprises: (2) the three-dimensional vehicle boundary frame is constructed by respectively determining three vanishing points in a scene according to lane lines, vehicle textures and street lamp positions in a traffic scene, and constructing the three-dimensional vehicle boundary frame by cutting lines to a vehicle Mask generated by Mask R-CNN based on the three vanishing points.
3. The method of claim 1, wherein the method comprises: (3) the vehicle virtual detection area is established in the lens visual field range, and whether the vehicle is in the detection area is judged according to whether the middle point of the front side of the bottom surface of the three-dimensional vehicle boundary frame is in the detection area.
4. The method of claim 1, wherein the method comprises: (4) the road surface calibration in (1) further obtains a homography matrix H between the road plane world coordinates and the corresponding pixel coordinates,
firstly, determining pixel coordinates of lane virtual line segment endpoints, establishing straight lines according to the lane virtual line segment endpoints and a second scene vanishing point, and then obtaining pixel coordinates of intersection points of the straight lines and solid lines on two sides of a lane; taking the points with the known pixel coordinates as road surface calibration reference points; the world coordinates of the reference points can be obtained by using the known actual length and lane width of the lane dotted line section; taking the pixel coordinates and world coordinates of the reference point into the formula (2), and solving eight independent parameters in the homography matrix by utilizing a least square method or singular value decomposition, thereby providing a basis for calculating the length and the speed of the vehicle based on the three-dimensional vehicle boundary frame;
in the formula (x)i,yi) And (X)i,Yi) Respectively representing the world coordinate and the pixel coordinate of the reference point i, and m is an independent parameter in the homography matrix H.
5. The method of claim 1, wherein the method comprises: (5) in the vehicle length calculation, the mid-point world coordinates of the front and back sides of the bottom surface of the three-dimensional vehicle boundary frame are calculated according to the homography matrix obtained by road surface calibration, and the difference value of the two mid-point world coordinates is the actual length of the vehicle.
6. The method of claim 1, wherein the method comprises: (6) in the vehicle speed calculation, the vehicle speed is calculated by using the distance traveled by the vehicle in the virtual detection area and the time, wherein the travel distance is calculated according to the world coordinate difference between the middle points of the front edges of the bottom surfaces of the three-dimensional vehicle boundary frames corresponding to the vehicle entering and leaving the detection area, and the travel time is determined according to the video frame rate, and the vehicle speed calculation formula is as follows:
in the formula: v is the vehicle speed, L and T are the driving distance and time of the vehicle in the virtual detection area, NfIs the sum of the corresponding running processes of the vehicle in the detection areaNumber of frames, FrIs the frame rate of the video and,
and
respectively, the world coordinates of the middle point of the front side of the bottom surface of the three-dimensional bounding box corresponding to the vehicle entering and leaving detection areas.
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| CN113689713A (en) * | 2020-05-19 | 2021-11-23 | 昆山研达电脑科技有限公司 | Vehicle speed monitoring method based on automobile data recorder |
| WO2021237750A1 (en) * | 2020-05-29 | 2021-12-02 | Siemens Ltd., China | Method and apparatus for vehicle length estimation |
| CN112489106A (en) * | 2020-12-08 | 2021-03-12 | 深圳市哈工交通电子有限公司 | Video-based vehicle size measuring method and device, terminal and storage medium |
| CN112798811B (en) * | 2020-12-30 | 2023-07-28 | 杭州海康威视数字技术股份有限公司 | Speed measurement method, device and equipment |
| CN113011388B (en) * | 2021-04-23 | 2022-05-06 | 吉林大学 | Vehicle outer contour size detection method based on license plate and lane line |
| CN114863025B (en) * | 2022-05-18 | 2023-03-10 | 禾多科技（北京）有限公司 | Three-dimensional lane line generation method and device, electronic device and computer readable medium |
| CN115019557B (en) * | 2022-06-09 | 2024-05-14 | 杭州电子科技大学 | Lane virtual boundary construction and boundary crossing detection method based on TUIO protocol |
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