关键词: AdaBoost-EHMM, 行为识别, 特征提取

Abstract: Hidden Markov Model（HMM） is an effective method of modeling time sequence, and has been widely used in speech recognition, character recognition, and in action recognition recently. Human action sequence is one kind of special time sequences. Each action sequence always includes some key poses. So, AdaBoost-EHMM（AdaBoost-Exemplar-based HMM） algorithm is presented and used in action recognition. AdaBoost method is used to select exemplars from action sequences as the mean values of observation probability model. Fusion of multiple classifiers is adopted to classify action sequence. Effectiveness of the proposed approach is demonstrated with experiments.

Key words: AdaBoost-Exemplar-based HMM（AdaBoost-EHMM）, action recognition, feature extraction