A Conditional Random Field with Loop and Its Inference Algorithm

摘要：

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 human 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 of experiments that show the proposed approach to outperform the linear-chain structure CRF and Hidden Markov Models (HMM) in terms of recognition rates.

关键词： Conditional Random Field Hidden Markov Models junction tree algorithms human motion recognition

作者: Zhu Wen-qiu Shao Xiang-jun

作者单位: School of Computer and Communication, Hunan University of Technology, Zhuzhou, Hunan, 412008, China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 11-14

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）