Multi-camera people tracking using evidential filters

Abstract

This work proposes a novel filtering algorithm that constitutes an extension of Bayesian particle filters to the Dempster–Shafer theory. Our proposal solves the multi-target problem by combining evidences from multiple heterogeneous and unreliable sensors. The modelling of uncertainty and absence of knowledge in our approach is specially attractive since it does not require to specify prior nor conditionals that might be difficult to obtain in complex problems.

The algorithm is employed to propose a novel solution to the multi-camera people tracking problem in indoor environments. For each particle, the evidence of finding the person being tracked at the particle location is calculated by each sensor. Sensors also provide a degree of evidence about their reliability. The reliability is calculated based on the visible portion of the targets and their occlusions. Evidences collected from the camera set are fused considering their reliability to calculate the best hypothesis. The experiments conducted in several environments show the validity of the proposal.