Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings

Abstract

We consider the problem of data association in a multi-person tracking context. In semi-crowded environments, people are still discernible as individually moving entities, that undergo many interactions with other people in their direct surrounding. Finding the correct association is therefore difficult, but higher-order social factors, such as group membership, are expected to ease the problem. However, estimating group membership is a chicken-and-egg problem: knowing pedestrian trajectories, it is rather easy to find out possible groupings in the data, but in crowded scenes, it is often difficult to estimate closely interacting trajectories without further knowledge about groups. To this end, we propose a third-order graphical model that is able to jointly estimate correct trajectories and group memberships over a short time window. A set of experiments on challenging data underline the importance of joint reasoning for data association in crowded scenarios.