Vehicle detection method based on mean shift clustering
To overcome the missing detection of traditional external feature based stereo vision vehicle detection method while there were multiple vehicles on the road, this paper proposed a road vehicle detection method based on mean shift clustering and semi-global disparity map. Firstly, the semi-global optimization function was established to find the WTA solution based on the principle of optimal in scan lines, and the sub-pixel accurate semi-global disparity map was obtained by quadratic curve fitting. Then, the three dimensional environment and the point cloud were reconstructed from the stereo camera calibration data and the disparity map. After that, a road plane extraction method was proposed based on the lane detection, and the ground point was removed. At last, based on the road vehicle feature analysis, the kernel function of Mean Shift clustering algorithm was optimized to realize the multiple vehicle positioning and detection. The real vehicle road experiment results indicated that this method has a good robustness to the road environment. Also it can effectively distinct multiple vehicle even if there is occlusion.