Abstract: For the problem of divergence of the classical particle filter method for multiple object tracking in image sequences, a new particle filter, the so-called Pseudo Particle Filter (PPF), is proposed. The PPF invokes subset particles of generic particle filters to form a continuous estimate of the posterior density function of the objects. After importance-sampling resampling(ISR), the subset particles converge to the observations. It is proved that, using the appropriate kernel function of the mean-shift algorithm, we can get the subset particles of the observations and the fixed points of clustering results as the state of the objects. A multi-object data association and state estimation technique is proposed to resolve the subset particles correspondence ambiguities that arise when multiple objects are present. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.

Key words: particle filter, object recognition, multi-object tracking, image processing