Probabilistic tracking using optical flow to resolve color ambiguities

Color-based tracking is prone to failure in situations where visually similar targets are moving in close proximity to each other. To deal with the ambiguities in color information we propose an additional color-independent feature based on the target’s local motion, which is calculated from the optical flow induced by the target in consecutive images. By modifying a color-based particle filter to account for the target’s local-motion, the hybrid color/local-motion-based tracker is constructed. The hybrid tracker was compared to a purely color-based tracker on a challenging data-set that involved near-collisions and complete occlusions between visually similar persons. The optical flow was estimated using a robust and a nonrobust method. The experiments show that even if a nonrobust method is used to estimate the optical flow, the local-motion feature largely resolves ambiguities caused by the visual similarity between persons.