Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking

Recently, fusion of low- and high-dimensional approaches shows its success in the generic human motion tracking. However, how to choose the trackers adaptively according to the motion types is still a challenging problem. This paper presents a trackers sampling approach for generic human motion tracking using both low- and high-dimensional trackers. Gaussian Process Dynamical Model(GPDM) is trained to learn the motion model of low-dimensional tracker, and it performs better on specific motion types. Annealed Particle Filtering(APF) shows its advantage in the tracking without limitation on motion types. We combine both of the two methods and automatically sample trackers according to the motion types that it is tracking on. To improve performance, trackers communication is adopt to keep the better state of trackers. The approach facilitates tracking of generic motions with low particle numbers.