Object contour tracking via adaptive data-driven kernel

Abstract

We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel shape as well as scale and orientation selection problem when the tracking targets are changing in size, the adaptive kernel can robustly achieve the adaptation to target variation and act toward the actual target contour simultaneously with the mean shift iterations. Level set technique is novelly introduced to the mean shift sample space to both cope with insufficient low-level information and implement the adaptive kernel evolution and update. Since the active contour model is designed to drive the kernel constantly to the direction that maximizes the appearance similarity, this adaptive kernel can continually seize the target shape to give a better estimation bias and produce accurate shift of the mean. Finally, accurate target region can successfully avoid the performance loss stemmed from pollution of background pixels hiding inside the kernel and qualify the samples fed the next time step. Experimental results on a numer of challenging sequences validate the effectiveness of the technique.