Apparatus and method for object detection and tracking and roadway awareness using stereo cameras
First Claim
1. A computer-implemented method for adaptive regulation of distance between a host vehicle and a lead vehicle, comprising:
- receiving, at an image processing unit, a stereo image of a roadway scene;
locating, using a road detection and awareness module at said image processing unit, said roadway scene in a headway direction of said host vehicle,computing, using said road detection and awareness module, a plurality of vehicle detection gates, wherein each of said vehicle detection gates specifies a region of interest (ROI) of the roadway scene of the stereo image and a depth interval between said vehicle detection gates of the roadway scene of the stereo image, said each vehicle detection gate overlaps a portion of an adjacent detection gate;
identifying, using said road detection and awareness module, at least two lane boundary markers in said stereo image of said roadway scene;
retrieving, using said road detection and awareness module, ground plane data, said lane boundary markers, and said depth interval/range between said vehicle detection gates;
computing, using said road detection and awareness module, depth for each said vehicle detection gate using the depth interval and computing said region of interest (ROI) for each said vehicle detection gate using the ground plane data and the lane marker positions, wherein said ROI is a projection in the image of the roadway scene of height and depth equal to the depth of the vehicle detection gate lying between two lane boundary makers and the ground;
detecting, using a vehicular detection and tracking module at said image processing unit, potential lead vehicle detections among said vehicle detection gates on said roadway scene in said headway direction of said host vehicle, wherein said detecting comprising searching for edge features/pixels for the potential lead vehicle in said depth range within said ROI;
receiving a plurality of left images and a plurality of right images of the stereo image for each said gate ROI;
sub-sampling said plurality of the left images and the right images along a horizontal axis; and
filtering said sub-sampled left images and said sub-sampled right images,applying a vertical sobel application to said sub-sampled left images to compute vertical left sobel edge images, wherein said vertical left sobel edge images are candidate image pixels for left edge pixels;
selecting said left candidate image pixels as said left edge pixels based on a threshold;
computing an intensity histogram for said selected left edge pixels to obtain a total number of said selected left sobel edge pixels and;
thresholding the vertical left sobel image using the intensity histogram for said selected right edge pixels to produce a left edge pyramid, wherein said left edge pyramid is a binary image comprising said selected left sobel edge pixels.
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Abstract
The present invention provides a collision avoidance apparatus and method employing stereo vision applications for adaptive vehicular control. The stereo vision applications are comprised of a road detection function and a vehicle detection and tracking function. The road detection function makes use of three-dimensional point data, computed from stereo image data, to locate the road surface ahead of a host vehicle. Information gathered by the road detection function is used to guide the vehicle detection and tracking function, which provides lead motion data to a vehicular control system of the collision avoidance apparatus. Similar to the road detection function, stereo image data is used by the vehicle detection and tracking function to determine the depth of image scene features, thereby providing a robust means for identifying potential lead vehicles in a headway direction of the host vehicle.
75 Citations
11 Claims
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1. A computer-implemented method for adaptive regulation of distance between a host vehicle and a lead vehicle, comprising:
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receiving, at an image processing unit, a stereo image of a roadway scene; locating, using a road detection and awareness module at said image processing unit, said roadway scene in a headway direction of said host vehicle, computing, using said road detection and awareness module, a plurality of vehicle detection gates, wherein each of said vehicle detection gates specifies a region of interest (ROI) of the roadway scene of the stereo image and a depth interval between said vehicle detection gates of the roadway scene of the stereo image, said each vehicle detection gate overlaps a portion of an adjacent detection gate; identifying, using said road detection and awareness module, at least two lane boundary markers in said stereo image of said roadway scene; retrieving, using said road detection and awareness module, ground plane data, said lane boundary markers, and said depth interval/range between said vehicle detection gates; computing, using said road detection and awareness module, depth for each said vehicle detection gate using the depth interval and computing said region of interest (ROI) for each said vehicle detection gate using the ground plane data and the lane marker positions, wherein said ROI is a projection in the image of the roadway scene of height and depth equal to the depth of the vehicle detection gate lying between two lane boundary makers and the ground; detecting, using a vehicular detection and tracking module at said image processing unit, potential lead vehicle detections among said vehicle detection gates on said roadway scene in said headway direction of said host vehicle, wherein said detecting comprising searching for edge features/pixels for the potential lead vehicle in said depth range within said ROI; receiving a plurality of left images and a plurality of right images of the stereo image for each said gate ROI; sub-sampling said plurality of the left images and the right images along a horizontal axis; and filtering said sub-sampled left images and said sub-sampled right images, applying a vertical sobel application to said sub-sampled left images to compute vertical left sobel edge images, wherein said vertical left sobel edge images are candidate image pixels for left edge pixels; selecting said left candidate image pixels as said left edge pixels based on a threshold; computing an intensity histogram for said selected left edge pixels to obtain a total number of said selected left sobel edge pixels and; thresholding the vertical left sobel image using the intensity histogram for said selected right edge pixels to produce a left edge pyramid, wherein said left edge pyramid is a binary image comprising said selected left sobel edge pixels. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method for adaptive regulation of distance between a host vehicle and a lead vehicle, comprising:
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receiving, at an image processing unit, a stereo image of a roadway scene; locating, using a road detection and awareness module at said image processing unit, said roadway scene in a headway direction of said host vehicle, computing, using said road detection and awareness module, a plurality of vehicle detection gates, wherein each of said vehicle detection gates specifies a region of interest (ROI) of the roadway scene of the stereo image and a depth interval between said vehicle detection gates of the roadway scene of the stereo image, said each vehicle detection gate overlaps a portion of an adjacent detection gate; identifying, using said road detection and awareness module, at least two lane boundary markers in said stereo image of said roadway scene; retrieving, using said road detection and awareness module, ground plane data, said lane boundary markers, and said depth interval/range between said vehicle detection gates; computing, using said road detection and awareness module, depth for each said vehicle detection gate using the depth interval and computing said region of interest (ROI) for each said vehicle detection gate using the ground plane data and the lane marker positions, wherein said ROI is a projection in the image of the roadway scene of height and depth equal to the depth of the vehicle detection gate lying between two lane boundary makers and the ground; detecting, using a vehicular detection and tracking module at said image processing unit, potential lead vehicle detections among said vehicle detection gates on said roadway scene in said headway direction of said host vehicle, wherein said detecting comprising searching for blobs for the potential lead vehicle in said depth range within said ROI; receiving a plurality of left images and a plurality of right images of the stereo image for each said gate ROI; sub-sampling said plurality of the left images and the right images along a horizontal axis; filtering said sub-sampled left images and said sub-sampled right images; selecting said bobs located inside said ROI; determining a maximum width of the ROI and a minimum width of the ROI; retrieving pairs of blobs from the selected blobs, wherein said retrieved pairs of blobs are at least two of the selected blobs having a vertical position to be same within a defined threshold and width of a bounding box of the selected blobs is between the maximum width and the minimum width of the ROI; and computing bounding boxes for all said retrieved blob pairs to define detection ROIs. - View Dependent Claims (9, 10, 11)
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Specification