Object Tracking Arithmetic Based on Importance Ordering Monte Carlo Particle Filtering

In order to obtain efficient object tracking under cluttered scenes, a method was proposed based on importance ordering Markov Chain Monte Carlo (MCMC) particle filtering. Firstly, a few authorized initial particles were made use to approximate the true posterior particle distribution. And then the new particles were drawn from the rough approximation with the proposed importance ordering MCMC sampling strategy to build several independent Markov Chains, corresponding one-to-one to the modes of the true posterior distribution, so as to approximate the multimode of the true posterior distribution. According to the current mode distribution, the method could establish several adaptive independent Markov Chains to approximate the posterior distribution of objects under multimode cluttered scenes. An importance ordering strategy was taken to make fully use of the history samples for state transfer decision, to increase the possibility that small weight samples could be selected and to decrease the probability that build process of Markov Chains got in local optimized. Simulation and verity experiment show that the proposed method can achieve stably and exactly object tracking, its performance is better than the standard particle filtering method and the MCMC particle filtering method.