Exploring structure for long-term tracking of multiple objects in sports videos

International audience<p>In this paper we propose a novel approach for exploringstructural relations to track multiple objects that may undergo long-termocclusion and abrupt motion. We use a model-free approach that reliesonly on annotations given in the first frame of the video to track allthe objects online, i.e. without knowledge from future frames. Weinitialize a probabilistic Attributed Relational Graph (ARG) from thefirst frame, which is incrementally updated along the video. Instead ofusing structural information only to evaluate the scene, the proposedapproach considers it to generate new tracking hypotheses. In this way,our method is capable of generating relevant object candidates that areused to improve or recover the track of lost objects. The proposed methodis evaluated on several videos of table tennis matches and on the ACASVAdataset. The results show that our approach is very robust, flexible andable to outperform other state-of-the-art methods in sports videos thatpresent structural patterns.</p