Inferring Dynamic Group Leadership Using Sequential Bayesian Methods

Abstract

In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups from observations. Unlike traditional leader-follower models, here we develop a new rotated leadership model that can capture the dynamic evolution of the interaction patterns in groups over time. Two methods, an online Gibbs sampler and deterministic particle filter, are then designed to infer sequentially the leader in group object tracking scenarios. Synthetic and real pigeon flocking data are used to demonstrate the effectiveness of the proposed techniques in terms of identifying the group leader under complex dynamics.