Object Tracking Using Grayscale Appearance Models and Swarm Based Particle Filter

Abstract. We propose a hybrid tracking algorithm consisting of two trackers built on grayscale appearance models. In a first tracker we employ an object template that consists of several grayscale image patches. Every patch votes for the possible positions of the object undergoing tracking. A grayscale appearance model that is learned on-line is used in a supplementing tracker. A particle swarm optimization algorithm is utilized to shift particles toward more promising regions in the probability density function. Experimental results show that the hybrid tracker outperforms each of the trackers