中文摘要：

人体步态识别作为一种远距离和非侵犯性的识别技术在视频监控等领域具有广泛的应用前景．基于此原因，文中提出基于连续密度隐马尔可夫模型（CD—HMM）的人体步态识别算法．首先，提出基于自然步态周期的特征提取算法，并在此基础上构造观测向量集．然后，使用从训练样本集中提取的步态向量集对CD-HMM进行参数估计．最后，提出基于Cox回归分析的渐进自适应算法对训练过的步态模型进行参数自适应和步态识别．实验表明，相比现有的其它步态识别算法，文中算法具有更高的识别率．

英文摘要：

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.