A robust multiple cues fusion based Bayesian tracker
Zhang, Xiaoqin; Liu, Zhiyong; Qiao, Hong
2007
会议名称IEEE International Conference on Robotics and Automation
会议录名称PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
会议日期APR 10-14, 2007
会议地点Rome, ITALY
摘要This paper presents an efficient and robust tracking algorithm based on multiple cues fusion in the Bayesian framework. This method characterizes the object to be tracked using a MOG (mixture of Gaussians) based appearance model and a chamfer-matching based shape model. A selective updating technique for the models is employed to accommodate for appearance and illumination changes. Meantime, the mean shift algorithm is embedded as the prior information into the Bayesian framework to give a heuristic prediction in the hypotheses generation process, which also alleviates the great computational load suffered by the conventional Bayesian tracker. Experimental results demonstrate that, compared with some existing works, the proposed algorithm has a better adaptability to changes of the object as well as the environments.
关键词Appearance Model Chamfer Distance Bayesian Tracker Template Update
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12819
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Qiao, Hong
作者单位Chinese Acad Sci, Inst Automat
推荐引用方式
GB/T 7714
Zhang, Xiaoqin,Liu, Zhiyong,Qiao, Hong. A robust multiple cues fusion based Bayesian tracker[C],2007.
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