Hybrid Conditional Random Fields for Multi-object Tracking with a Mobile Robot

摘要：

As the precondition to perform many tasks including person following and dynamic obstacle avoiding, object tracking is very important for mobile robot systems especially in populated dynamic environment. A novel hybrid conditional random field model which has a hierarchical structure and includes hidden states is proposed for multi-object tracking with a mobile platform. Since conditional random field is a kind of discriminative model which makes no assumptions about the dependency structure between observations and allows non-local dependencies between state and observations. The proposed method cannot only integrate moving object detection and tracking perfectly well, but also can fuse multiple cues including shape information and motion information to improve the stability of tracking. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precise and stability than JPDAF.

关键词： conditional random fields object tracking object detection information fusion

作者: Luo Ronghua Min Huaqing

作者单位: Department of Computer Science and Engineering South China University of Technology Guangzhou,Guangd Department of Software South China University of Technology Guangzhou,Guangdong 510006,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）