A Particle Filtering for 3D Human Hand Tracking

Zhiquan Feng, Bo Yang, Yuehui Chen, Yan Jiang, Tao Xu, Haokui Tang

Abstract: A novel sampling method is put forward in this paper. Firstly, under specific human-computer conditions, both cognitive psychology features of operators and motion features of the operators' hands are studied, upon which a basic assumption is made. Then, a sampling algorithm is put forward. Finally, in order to demonstrate validity and performance of our algorithm, a great deal of experiments is completed. By using fewer particles, compared with conventional particle filtering, the approach presented in this paper may achieve better tracking precision. In forms of both the theoretical analysis and a great deal of experimental results from real video data, just a few particles are needed to describe post probability distribution of state variables without reducing the tracking precision by our dimensionality reduction method.