Multi-Information Fusion for Scale Selection in Robot Tracking
Zhang, Xiaoqin; Qiao, Hong; Liu, Zhiyong; Zhang, Xiaoqin)
2006
Conference NameIEEE/RSJ International Conference on Intelligent Robots and Systems
Source Publication2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS
Conference DateOCT 09-13, 2006
Conference PlaceBeijing, PEOPLES R CHINA
AbstractMean shift, for its simplicity and efficiency, has achieved a considerable success in robot tracking. For the mean shift based tracking algorithm, the scale of the mean-shift kernel bandwidth is a crucial parameter which reflects the size of tracking window. However, in literature how to properly update or select the bandwidth remains a tough task as the size of the object under consideration changes. In this paper, a weighted average integral projection approach is proposed to extract the local information of the object, and then a multi-information fusion strategy is suggested for the scale selection, which combines both the global and local information of the sample weight image. Moreover, a coarse-to-fine approximate approach is employed to accelerate the procedure. Experimental results demonstrate that, compared to some existing works, the strategy proposed has a better adaptability as the size of the object changes in clutter environments.
KeywordMean Shift Integral Projection Kernel Bandwidth
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12824
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 AuthorZhang, Xiaoqin)
AffiliationChinese Acad Sci, Inst Automat
Recommended Citation
GB/T 7714
Zhang, Xiaoqin,Qiao, Hong,Liu, Zhiyong,et al. Multi-Information Fusion for Scale Selection in Robot Tracking[C],2006.
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