Sparse structure inference for group and network tracking

This paper presents a method for inferring interaction strength and structure amongst targets in multiple target tracking (MTT) applications. By making simple assumptions, it is shown how an efficient and well-mixing MCMC inference method can be developed to learn about the relationships between tracked targets, including leader-follower relationships, group relationships and the influence of targets on others. This network structure of influence between targets is inferred in a sparse way, setting many interaction terms to zero and allowing for more efficient inference and clearer structural conclusions to be drawn. The effectiveness of the method is demonstrated on both synthetic and real animal flocking data.