Using action classification for human-pose estimation

This paper presents a 3D-point-cloud system that extracts a 3D-point-cloud feature (VISH) from the observation of a depth sensor to reduce feature/depth ambiguity and estimates human poses using the result of action classification and a kinematic model. Based on the concept of distributed representation, a non-parametric action-mixture model is proposed in the system to represent high-dimensional human-pose space using low-dimensional manifolds in searching human poses. In each manifold, the probability distribution is estimated by the similarity of features. The distributions in the manifolds are then redistributed according to the stationary distribution of a Markov chain that models the frequency of actions. After the redistribution, the manifolds are combined according to the distribution determined by the action classification. In addition, the spatial relationship between human-body parts is explicitly modeled by a kinematic chain. Computer-simulation results showed that multiple low-dimensional manifolds can represent human-pose space. The 3D-point-cloud system showed reduction of the overall error and standard deviation compared with other approaches without using action classification.