Particle Swarm Optimization Based Object Tracking

Abstract

This paper proposes a particle swarm optimization based algorithm for object tracking in image sequences. The parametric models of variability of the object appearance are employed to shift the particle swarm in order to cover the promising object location. Afterwards the particles are drawn from a Gaussian distribution. Then the particle swarm optimization takes place in order to concentrate the particles near the true object state. A grayscale appearance model that is learned online is utilized in evaluation of the particles score. Experimental results thatwere obtained in a typical office environment show the feasibility of our approach, especially when the object undergoing tracking has a rapid motion or the appearance changes are considerable. The resulting algorithm runs in real-time on a standard computer.