An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes

Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thorougly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions: A Bayesian framework for tracking an unknown and varying number of persons, a variational expectation-maximization (VEM) algorithm with closed-form expressions for the posterior distributions and model parameter estimation, A method capable of exploiting observations from multiple detectors, thus enabling multimodal fusion, and built-in object-birth and object-visibility processes that allow to handle person appearance and disappearance. The method is evaluated on standard datasets, which shows competitive and encouraging results with respect to state of the art methods.