In this paper, we address the task of tracking groups of people in surveillance scenarios. This is a major challenge in computer vision, since groups are structured entities, subjected to repeated split and merge events. Our solution is a joint individual-group tracking framework, inspired by a recent technique dubbed decentralized particle filtering. The proposed strategy factorizes the joint individual-group state space in two dependent subspaces where individuals and groups share the knowledge of the joint individual-group distribution. In practice, we establish a tight relation of mutual support between the modeling of individuals and that of groups, promoting the idea that groups are better tracked if individuals are considered, and viceversa. Extensive experiments on a published and novel dataset validate our intuition, opening up to many future developments.
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