华南理工大学学报(自然科学版) ›› 2011, Vol. 39 ›› Issue (5): 61-67.doi: 10.3969/j.issn.1000-565X.2011.05.011

• 计算机科学与技术 • 上一篇    下一篇

用于移动机器人多目标跟踪的混合条件随机场

罗荣华 闵华清   

  1. 华南理工大学 计算机科学与工程学院,广东 广州 510006
  • 收稿日期:2010-01-25 修回日期:2011-01-17 出版日期:2011-05-25 发布日期:2011-04-01
  • 通信作者: 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
  • 作者简介:罗荣华(1975-),男,博士,副教授,主要从事智能机器人研究
  • 基金资助:

    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)

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)

摘要: 根据移动机器人系统多目标跟踪的特点,提出了一种包括运动检测、数据关联和目标状态估计3个层次的多目标跟踪混合条件随机场.作为一种辨别式模型,该混合条件随机场模型允许状态与数据之间存在非局部的依赖关系,不仅可以利用目标的运动信息和局部形状信息提高多目标跟踪中数据关联的精度,而且可以利用多次观测数据检测新目标,实现新目标检测与目标跟踪的同步.在自行研发的移动机器人平台上的多目标跟踪实验结果表明,基于文中提出的混合条件随机场的移动机器人多目标跟踪方法比基于产生式模型的方法JPDAF具有更高的精度与稳定性.

关键词: 移动机器人, 多目标跟踪, 条件随机场, 目标检测, 信息融合

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