Predicting social interactions for visual tracking

Human interaction dynamics are known to play an important role in the development of robust pedestrian trackers that are applicable to a variety of applications in video surveillance. Traditional approaches to pedestrian tracking assume that each pedestrian walks independently and the tracker predicts the location based on an underlying motion model, such as a constant velocity or autoregressive model. Recent approaches have begun to leverage interaction, especially by modeling the repulsion force, among pedestrians to improve motion predictions. However, human interaction is more complex and is influenced by both repulsion and attraction effects. This motivates the use of a more complex human interaction model for pedestrian tracking. In this paper, we propose a novel visual tracking method by leveraging complex social interactions. We present an algorithm that decomposes social interactions into multiple potential interaction modes. We integrate these multiple social interaction modes into an interactive Markov Chain Monte Carlo tracker. We demonstrate how the developed method translates into a more informed motion prediction, resulting in a robust tracking performance. We test our method on videos from unconstrained outdoor environments and compare it against popular multi-object trackers.