Combining Differential Evolution with Particle Filtering for Articulated Hand Tracking from Single Depth Images

Tracking articulated hand motion from visual observations is challenging mainly due to the high dimensionality of the state space. Dense sampling is difficult to be performed in such high-dimensional space, so the traditional particle filtering can’t track articulated motion well. In this paper, we propose a new algorithm by combining differential evolution with a particle filter, to track the articulated motion of a hand from single depth images captured by a Kinect sensor. Through the optimization procedure of differential evolution, the particles are moved to the regions with a high likelihood. Only single depth information is used as the input, so our method is immune to illumination and background changes. The tracking system is developed with OpenSceneGraph (OSG). Experiments based on both synthetic and real image sequences demonstrate that the proposed method is capable of tracking articulated hand motion accurately and robustly.