Real-time robust tracking via sparse representation: A mode-seeking approach

In this paper, we propose a robust realtime tracking as a mode seeking process over likelihood map via sparse representation. In order to estimate the likelihood map, the target and candidate models were represented as overlapping patches. The likelihood map of the target candidate is obtained by sparsely representing the candidate patches in the space spanned by target patches and trivial bases. The object is localized by iteratively seeking the mode of this likelihood map. Since the mode-seeking process localizes the object in few iterations, it achieves realtime speed. Since the local patches are less sensitive to global appearance and illumination changes, the proposed approach shows robustness to the aforementioned challenges. We quantify the performance of the proposed tracker on many video sequences with various challenges involving occlusion, illumination change and pose variations. The proposed approach shows excellent performance in terms of robustness and speed compared to other trackers.