Multi-Information Fusion for Scale Selection in Robot Tracking

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Multi-Information Fusion for Scale Selection in Robot Tracking
	Zhang, Xiaoqin; Qiao, Hong; Liu, Zhiyong; Zhang, Xiaoqin)
	2006
会议名称	IEEE/RSJ International Conference on Intelligent Robots and Systems
会议录名称	2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS
会议日期	OCT 09-13, 2006
会议地点	Beijing, PEOPLES R CHINA
摘要	Mean 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.
关键词	Mean Shift Integral Projection Kernel Bandwidth
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12824
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Zhang, Xiaoqin)
作者单位	Chinese Acad Sci, Inst Automat
推荐引用方式 GB/T 7714	Zhang, Xiaoqin,Qiao, Hong,Liu, Zhiyong,et al. Multi-Information Fusion for Scale Selection in Robot Tracking[C],2006.