Modeling local behavior for predicting social interactions towards human tracking

Human interaction dynamics are known to play an important role in the development of robust pedestrian trackers that are needed for 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 forces among pedestrians to improve motion predictions. However, human interaction is more complex and is influenced by multiple social effects. This motivates the use of a more complex human interaction model for pedestrian tracking. In this paper, we propose a novel human tracking method by modeling 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 and demonstrate how the developed method translates into a more informed motion prediction, resulting in robust tracking performance. We test our method on videos from unconstrained outdoor environments and evaluate it against common multi-object trackers.