Abstract：As a remote and indirect recognition technology, human gait recognition has extensive applications in various fields, such as video-based surveillance systems. In this paper, the continuous density hidden Markov models (CD-HMM) is employed to perform gait recognition. Firstly, a feature extraction algorithm is proposed based on natural gait cycles,and the observation vector set is constructed using the extracted features. Then, the gait vector set extracted from the training sample set is used to estimate the parameters of CD-HMM. Finally, an adaptive algorithm is introduced based on Cox regression analysis to adaptively adjust parameters of the trained gait model. Experimental results show that the proposed method produces higher accuracies compared with other methods.
王修晖，严珂. 基于连续密度隐马尔可夫模型的人体步态识别*[J]. 模式识别与人工智能, 2016, 29(8): 709-716.
WANG Xiuhui, YAN Ke. Human Gait Recognition Using Continuous Density Hidden Markov Models. , 2016, 29(8): 709-716.
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2016, Vol. 29 
