Associating Groups of People

In a crowded public space, people often walk in groups, either with people they know or strangers. Associating a group of people over space and time can assist understanding individual’s behaviours as it provides vital visual context for matching individuals within the group. Seemingly an ‘easier’ task compared with person matching given more and richer visual content, this problem is in fact very challenging because a group of people can be highly non-rigid with changing relative position of people within the group and severe self-occlusions. In this paper, for the first time, the problem of matching/associating groups of people over large space and time captured in multiple non-overlapping camera views is addressed. Specifically, a novel people group representation and a group matching algorithm are proposed. The former addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views. In addition, we demonstrate a notable enhancement on individual person matching by utilising the group description as visual context. Our methods are validated using the 2008 i-LIDS Multiple-Camera Tracking Scenario (MCTS) dataset on multiple camera views from a busy airport arrival hall.