关键词: 条件随机域, 隐马尔可夫模型, 联合树算法, 动作识别

Abstract: A new algorithm for human motion recognition based on Conditional Random Fields（CRFs） and Hidden Markov Models（HMM）—HMCRF is proposed.Most existing approaches to humman motion recognition with hidden states employ a Hidden Markov Model or suitable variant to model motion streams；a significant limitation of these models is the requirement of conditional independence of observations.In contrast，conditional models like the CRFs seamlessly represent contextual dependencies，support efficient，exact inference using dynamic programming，and their parameters can be trained using convex optimization.We introduce conditional graphical models as complementary tools for human motion recognition and present an extensive set，experiments show that the proposed approach outperforms the linear-chain structure CRF and HMM in terms of recognition rates.

Key words: Conditional Random Fields（CRFs）, Hidden Markov Models（HMM）, junction tree algorthms, human motion recognition