Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing

We propose a parallelized Annealed Particle Filter method via heterogeneous computing (P-APF), to implement real-time marker-less motion tracking based on OpenCL framework. The overall computing procedure in P-APF is decomposed into several computational tasks with corresponding granularity. According to the degree of parallelism, the tasks are assigned to standard and attached processors respectively, to fully leverage heterogeneous computing ability. A task latency hidden strategy is used to further reduce time cost. Experiments on different human motion datasets demonstrate that P-APF can achieve real-time tracking performance without losing accuracy. With an average acceleration ratio of 106 compared to serial implementation, the time cost basically remains constant with the growth of particle number and view number in a limited range.