Grid Particle Filter for Human Head Tracking Using 3D Model

A new 3D head-shoulder model based particle filter is presented for finding human head in static images. Edge cues are used as the likelihood function of the proposed particle filter. The positions of head as well as its direction are evaluated simultaneously. At each time step, the proposed algorithm generates a discretized grid state space and takes a subspace of it as the searching space of the particles. Hence the number of particles is well limited and the performance of the particle filter is improved. The experimental results prove that our method is effective and robust.