A robust multiple cues fusion based Bayesian tracker
Zhang, Xiaoqin; Liu, Zhiyong; Qiao, Hong
2007
Conference NameIEEE International Conference on Robotics and Automation
Source PublicationPROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
Conference DateAPR 10-14, 2007
Conference PlaceRome, ITALY
AbstractThis 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.
KeywordAppearance Model Chamfer Distance Bayesian Tracker Template Update
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12819
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorQiao, Hong
AffiliationChinese Acad Sci, Inst Automat
Recommended Citation
GB/T 7714
Zhang, Xiaoqin,Liu, Zhiyong,Qiao, Hong. A robust multiple cues fusion based Bayesian tracker[C],2007.
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