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

摘要： 提出一种新的基于条件随机域和隐马尔可夫模型（HMM）的人类动作识别方法——HMCRF。目前已有的动作识别方法均使用隐马尔可夫模型及其变型，这些模型一个最突出的不足就是要求观察值相互独立。条件模型很容易表示上下文相关性，且可使用动态规划做到有效且精确的推论，它的参数可以通过凸函数优化训练得到。把条件图形模型应用于动作识别之上，并通过大量的实验表明，所提出的方法在识别正确率方面明显优于一般线性结构的CRF和HMM。

关键词: 条件随机域, 隐马尔可夫模型, 联合树算法, 动作识别