Synergistic Change Detection and Tracking

Visual tracking in image streams acquired by static cameras is usually based on change detection and recursive Bayesian estimation, such an approach laying at the core of many practical applications. Yet, the interaction between the change detector and the Bayesian filter is typically designed heuristically. Differently, this paper develops a sound framework to model and implement a bidirectional communication flow between the two processes. In our Bayesian loop, change detection provides well-defined observation likelihood to the recursive filter and the filter prediction provides an informative prior to the change detector, which deploys Bayesian reasoning alike. The loop is developed for the two major variants of Bayesian filters used in tracking, namely the Kalman filter and the particle filter. Experiments on publicly available videos and a novel challenging data set show that the proposed interaction scheme outperforms several state-of-the-art trackers.