Scaling and occlusion robust athlete tracking in sports videos

Abstract

This paper proposes a novel approach to athlete tracking in sports videos. It follows the framework of Compressive Tracking (CT), but extends it by two manners, i.e. scale refinement as well as occlusion recovery. For the former, an objectness method, namely Edge Box (EB) is adopted to generate proposals, replacing the fixed sampling box in CT, which better fits the scales of the candidate objects. For the latter, a candidate obstruction based solution is presented, which makes use of additional trackers to detect possible obstructions especially the ones possessing highly similar appearances as the target one, and relocate the target as occlusion ends. Therefore, the proposed method inherits the advantage of CT in robust object modelling and fast processing speed, and embodies the tolerance to occlusion and scaling. We evaluate the proposed method on a collection of videos of beach volleyball games, and the experimental results and the comparison with recent advanced trackers highlight its effectiveness.