Gaussian mixture probability hypothesis density for visual people racking

Abstract

This paper presents our work which involves the application of a recursive Bayesian filter, the Gaussian mixture probability hypothesis density (GMPHD) filter, to a visual tracking problem. Foreground objects are detected using statistical background modeling to obtain measurements which are input into the filter. The GMPHD filter explicitly models the birth, survival and death of objects by managing the number of Gaussian components and jointly estimates the time-varying number of objects and their states. A scene-driven method is proposed to initialize the GMPHD filter and model the birth of new objects. The results shows when a person or a group appeared, merged, split, and disappeared in the field of view, the GMPHD filter can track the number and positions at the most time. The scene-driven GMPHD filter can track the birth of new objects faster than the particle PHD filter.