The PSO-Based Adaptive Window for People Tracking

This paper presents a robust tracking algorithm using an adaptive tracking window associated with five parameters, where the parameters of the tracking window are optimized by a particle swarm optimization (PSO) algorithm. Basically, the optimization of a tracking window is transformed into a searching algorithm in a five-dimension feature space, which constrains the possibilities of the window. Particles associated with different parameters fly around the searching space independently, while they are sharing information from the society and adjust their behaviors to achieve the global optimization, which means the most optimized parameters for the tracking window. Appearance histogram is employed to calculate the fitness function for particles, where the distance between histograms is measured by histogram intersection. Estimated people motion is utilized to expedite the convergence of particles. Experimental results of people tracking demonstrate that the algorithm is efficient, robust, and adaptive to various rigid and non-rigid people motions