Hidden Markov Model-based human action recognition using mixed features

作者：Ji Xiaofei^*; Wang Ce; Li Yibo; Wu Qianqian

来源：Journal of Computational Information Systems, 2013, 9(9): 3659-3666.

DOI：10.12733/jcis5950

• Action recognition
• Feature representation
• Hidden markov
• Hidden Markov model(HMM)
• Human-action recognition
• Mixed feature
• Parameters selection
• Recognition algorithm

Action recognition is a complex and challenging research that has received more and more attention in computer vision area. Action feature representation and recognition algorithm selection play the key roles in action recognition. On the basis of studying the representation and recognition of human actions, and giving full consideration to the advantages and disadvantages of different features and recognition algorithm, A novel Hidden Markov Model(HMM)based method using mixed features of silhouette and optical flow was proposed in this paper. The effectiveness of the features and HMM parameters selection on action recognition accuracy was discussed. Finally the proposed method was tested on the public Weizmann dataset. Experimental results show the method can achieve 100% correct recognition rate and outperform most existing methods.

• 出版日期2013