Non-rigid object tracking by adaptive data-driven kernel

Abstract

We derive an adaptive data-driven kernel in this paper to simultaneously address the kernel scale/orientation selection problem as well as the constant kernel shape in deformable object tracking applications. Level set technique is novelly introduced into the mean shift sample space to implement kernel evolution and update. Since the active contour model is designed to drive the kernel constantly to the direction that maximizes target likelihood, the kernel can adapt to target shape variation simultaneously with the mean shift iterations. Thus, it can give a better estimation bias to produce accurate shift of the mean and successfully avoid performance loss stemmed from pollution of the non-object regions hiding inside the kernel. Experimental results on a number of challenging sequences validate the effectiveness of the technique.