Object tracking in low-framerate video

In this paper, we present an object detection and tracking algorithm for low-frame-rate applications. We extend the standard mean-shift technique such that is is not limited within a single kernel but uses multiple kernels centered around high motion areas obtained by change detection. We also improve the convergence properties of the mean-shift by integrating two additional likelihood terms using object templates. Our simulations prove the effectiveness of the proposed method both under heavy occlusions and low frame rates down to 1-fps