Robust Multiple-People Tracking Using Colour-Based Particle Filters

Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle ltering, where occlusions are handled considering the target’s predicted trajectories. Model drift is tackled by careful updating, based on the history o ikelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using se quences from the CAVIAR database.