Visual tracking based on fragment-based correntropy induced metric

This paper proposes a fast and effective template-based visual tracking method based on a novel distance metric. First, a fragment-based correntropy induced metric (FCIM) is proposed, which exploit the strengths from both the correntropy method and the fragment scheme to handle observation noise (especially, gross errors caused by occlusion). Second, the proposed FCIM method is integrated into the Bayesian inference framework for solving the tracking problem. In addition, a simple on-line template update scheme is introduced to capture the appearance change of the object during the tracking processing. Experimental results and discussions demonstrate that the proposed method is better than other popular algorithms.