An Optimized Particle Filter Based on Improved MCMC Sampling Method

Particle filter (PF) is used in the three-dimensional (3D) free hand tracking system, which is nonlinear and non-Gaussian. Markov chain Monte Carlo (MCMC) plays a positive role in Bayesian statistical calculation and the maximum likelihood estimation. This paper focuses on using of MCMC algorithm in the PF sampling to reduce the time cost. The 3D free hand tracking system is real-time by using the improved PF algorithm. First, do experiments in the virtual platform with data gloves and establish constraints of 3D free hand. Second, we analyze the obtained data to get the sampling model, which is applied into the PF algorithm. Finally, use VC++ to code the algorithm in 3D gesture tracking system, and then compare with correlation algorithms. The results show that the cost of time is reduced by more than 15 % than the human gesture part recognition sample method (HGPRS) with the high tracking accuracy.