  In this paper, we propose a novel approach for exploiting structural
relations to track multiple objects that may undergo long-term occlusion and
abrupt motion. We use a model-free approach that relies only on annotations
given in the first frame of the video to track all the objects online, i.e.
without knowledge from future frames. We initialize a probabilistic Attributed
Relational Graph (ARG) from the first frame, which is incrementally updated
along the video. Instead of using the structural information only to evaluate
the scene, the proposed approach considers it to generate new tracking
hypotheses. In this way, our method is capable of generating relevant object
candidates that are used to improve or recover the track of lost objects. The
proposed method is evaluated on several videos of table tennis, volleyball, and
on the ACASVA dataset. The results show that our approach is very robust,
flexible and able to outperform other state-of-the-art methods in sports videos
that present structural patterns.
