Non-Rigid Object Tracking by Anisotropic Kernel Mean Shift
Huang Xian-wu · 天津大学学报：英文版 · 2007
Mean shift,an iterative procedure that shifts each data point to the average of data points in its neighborhood,has been applied to object tracker.However,the traditional mean shift tracker by isotropic kernel often loses the object with the changing object structure in video sequences,especially when the object structure varies fast.This paper proposes a non-rigid object tracker by anisotropic kernel mean shift in which the shape,scale,and orientation of the kernels adapt to the changing object structure.The experimental results show that the new tracker is self-adaptive and approximately twice faster than the traditional tracker,which ensures the robustness and real time of tracking.