Bidirectional Markov chain Monte Carlo particle filter for articulated human motion tracking

A novel framework of particle filter, named bidirectional Markov chain Monte Carlo particle filter (BMCMCPF), has been proposed to estimate articulated human movement state and action category jointly. Owing to the reason that we regard action category as the estimated state in our framework, firstly the motion models for every possible action are built via autoregressive modeling for the captured motion data with minimum distance. Meanwhile, the dynamic model and observation model also get coupled so that tracking and recognition can achieve synchronously. Then, the state estimation is completed by using the bidirectional Marko chain Monte Carlo sampling. BMCMCPF can not only improve the tracking performance as its global optimization property, but also smooth the joint’s movement trajectories to ensure the motion coordination. The experimental results on HumanEva datasets show that the effectiveness of BMCMCPF with unknown motion modality in solving the tracking problem.