Vehicle detection and tracking using Mean Shift segmentation on semi-dense disparity maps
Sébastien Lefebvre, Sébastien Ambellouis · 2012
This paper describes an original joint obstacle detection and tracking method based on a Mean Shift algorithm and semi-dense disparity maps. The semi-dense disparity maps are computed with a local 1D fuzzy scanline stereo matching approach. Each map is associated to a confidence map that is used to remove bad matches. The Mean Shift algorithm is applied to simultaneously extract each vehicle and track the 3D points belonging to the same vehicle along the sequence. We show that several vehicles can be efficiently detected and that a semi-dense disparity map is sufficient to reach an accurate segmentation even when occlusions occur. This paper presents some results on real image sequences acquired in the context of Advanced Driver Assistance Systems.