关键词: 动作识别, 肢体角度模型, 隐马尔可夫模型, 条件限制玻尔兹曼兹曼机, 深度置信网络

Abstract: The action recognition enables the machine to discriminate and understand the intention of the human action. And then it realizes efficient human-machine interaction. A new limb angle model is proposed to express the human action in 3-D space. The model has certain invariability and low computational complexity. In view of the traditional action recognition method based on Gaussian Mixture Model and Hidden Markov Model（GMM-HMM）, an action recognition model is proposed combining the Deep Belief Network（DBN） model and Hidden Markov Model（HMM）. A nonlinear DBN model based on the condition restricted Boltzmann machine（CRBM） is constructed. It has strong capable to model because of its deep structure, and it combines with historical information. It is more suitable for action recognition. Experiments results show that the algorithm has higher recognition rate, and it is feasible.

Key words: action recognition, limb angle model, Hidden Markov Mode（HMM）, Conditional Restricted Boltzman Machine（CRBM）, deep belief network