Intention-Aware Multiple Pedestrian Tracking

Even though pedestrian motion may look chaotic in most of the cases, recent studies have shown that this motion is mainly ruled by environment and social aspects. In this paper, we propose an interacting multiple model pedestrian tracking framework that incorporates these semantic considerations as a prior knowledge about intentions and interactions between targets. We consider 4 cases of motion for pedestrians: going straight, finding one's way, walking around and standing still. Those models are competing within an Interacting Multiple Model Particle Filter strategy. Targets interactions are handled with social forces, included as potential functions in the weighting process of the Particle Filter (PF). We use different social force models in each motion model to handle high level behaviors (collision avoidance, flocking...). We evaluate our algorithm on challenging datasets and demonstrate that such semantic information improves the tracker performance.