Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (5): 61-67.doi: 10.3969/j.issn.1000-565X.2011.05.011

• Computer Science & Technology • Previous Articles     Next Articles

Hybrid Conditional Random Field for Multi-Object Tracking of Mobile Robot

Luo Rong-hua  Min Hua-qing   

  1. School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China
  • Received:2010-01-25 Revised:2011-01-17 Online:2011-05-25 Published:2011-04-01
  • Contact: 罗荣华(1975-)，男，博士，副教授，主要从事智能机器人研究 E-mail:rhluo@scut.edu.cn
  • About author:罗荣华(1975-)，男，博士，副教授，主要从事智能机器人研究
  • Supported by:

    国家自然科学基金资助项目(61005061，60873078)；华南理工大学中央高校基本科研业务费资助项目(2009ZM0123)

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Abstract:

According to the characteristics of multi-object tracking of mobile robots,a hybrid conditional random field（HCRF） model,which consists of three layers including the motion detection layer,the data association layer and the state estimation layer,is proposed.As a kind of discriminative model that allows nonlocal dependencies between the state and the observation data,the proposed model can not only utilize local motion information and shape information to improve the accuracy of data association according to the motion information and the local shape information of the object,but also integrate object tracking and moving object detection according to the observation data obtained in multiple time steps.Experimental results of the multi-object tracking of the self-developed mobile robot show that the tracking based on the proposed HCRF model is more accurate and stable than that of the JPDAF method based on the generative model.

Key words: mobile robot multi-object tracking, conditional random fieldobject detection, information fusion