Multi-Person Bayesian Tracking with Multiple Cameras

Object tracking is an important task within the field of computer vision, which is driven by the need to detect interesting moving objects in order to analyze and recognize their behaviours and activities. However, tracking multiple object is a complex task due to a large number of issues number ranging from the different types of sensing set-up to the complexity of the object appearance and behaviours. In this chapter, we analyze some of the important issues to solve for multiple object tracking, reviewing briefly how they are addressed in the literature. We then present a state-of-the-art algorithm for the tracking of a variable number of 3D persons in a multi-camera setting with partial field-of-view overlap. The algorithm illustrates how in a Bayesian framework the raised issues can be formulated and handled. More specifically, the tracking problem relies on a joint multi-object state space formulation with individual object states defined in the 3D world. It involves several key features for efficient and reliable tracking like the definition of appropriate multiobject dynamics and a global multi-camera observation model based on color and foreground measurements, the use of the Reversible-Jump Markov Chain Monte Carlo (RJ-MCMC) framework for efficient optimization, the exploitation of powerful human detector outputs in the MCMC proposals to automatically initialize/update object tracks. Experimental results on challenging real-world tracking sequences and situations demonstrate the efficiency of such an approach.