Bayesian visual surveillance: A model for detecting and tracking a variable number of moving objects

An automatic detection and tracking framework for visual surveillance is proposed, which is able to handle a variable number of moving objects. Video object detectors generate an unordered set of noisy, false, missing, split, and merged measurements that make extremely complex the tracking task. Especially challenging are split detections (one object is split into several measurements) and merged detections (several objects are merged into one detection). Few approaches address this problem directly, and the existing ones use heuristics methods, or assume a known number of objects, or are not suitable for on-line applications. In this paper, a Bayesian Visual Surveillance Model is proposed that is able to manage undesirable measurements. Particularly, split and merged measurements are explicitly modeled by stochastic processes. Inference is accurately performed through a particle filtering approach that combines ancestral and MCMC sampling. Experimental results have shown a high performance of the proposed approach in real situations.