Multi-camera tracking using a Multi-Goal Social Force Model

Abstract

Tracking across non-overlapping cameras is a challenging open problem in video surveillance. In this paper, we propose a novel target re-identification method that models movements in non-observed areas with a modified Social Force Model (SFM) by exploiting the map of the site under surveillance. The SFM is developed with a goal-driven approach that models the desire of people to reach specific interest points (goals) of the site such as exits, shops, seats and meeting points. These interest points work as attractors for people movements and guide the path predictions in the non-observed areas. We also model key regions that are potential intersections of different paths where people can change the direction of motion. Finally, the predictions are linked to the trajectories observed in the next camera view where people reappear. We validate our multi-camera tracking method on the challenging i-LIDS dataset from the London Gatwick airport and show the benefits of the Multi-Goal Social Force Model.