计算机工程与应用Issue(14)：186-192,7.DOI:10.3778/j.issn.1002-8331.1111-0297
AdaBoost-EHMM算法及其在行为识别中的应用
AdaBoost-EHMM algorithm and application in action recognition.
谷军霞 1林润生 1王省1
作者信息
- 1. 国家气象信息中心 系统工程室，北京 100081
- 折叠
摘要
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.关键词
AdaBoost-EHMM/行为识别/特征提取Key words
AdaBoost-Exemplar-based HMM(AdaBoost-EHMM)/action recognition/feature extraction分类
信息技术与安全科学引用本文复制引用
谷军霞,林润生,王省..AdaBoost-EHMM算法及其在行为识别中的应用[J].计算机工程与应用,2013,(14):186-192,7.
