Robust Edge-Enhanced Fragment Based Normalized Correlation Tracking in Cluttered and Occluded Imagery

Correlation trackers are in use for the past four decades. Edge based correlation tracking algorithms have proved their strength for long term tracking, but these algorithms suffer from two major problems: clutter and slow occlusion. Thus, there is a requirement to improve the confidence measure regarding target and non-target object. In order to solve these problems, we present an “Edge Enhanced Fragment Based Normalized Correlation (EEFNC)” algorithm, in which we: (1) divide the target template into nine non-overlapping fragments after edge-enhancement, (2) correlate each fragment with the corresponding fragment of the template-size section in the search region, and (3) achieve the final similarity measure by averaging the correlation values obtained for every fragment. A fragment level template updating method is also proposed to make the template adaptive to the variation in the shape and appearance of the object in motion. We provide the experimental results which show that the proposed technique outperforms the recent Edge-Enhanced Normalized Correlation (EENC) tracking algorithm in occlusion and clutter.