Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker
	Suiwu Zheng; Linshan Liu; Hong Qiao
	2014
会议名称	Neural Information Processing. 21st International Conference, ICONIP 2014
会议日期	3-6 Nov. 2014
会议地点	Kuching, Malaysia
关键词	NONE
通讯作者	Suiwu Zheng
英文摘要	Robust object tracking in crowded and cluttered dynamic scenes is a very difficult task in robotic vision due to complex and changeable environment and similar features between the background and foreground. In this paper, a saliency feature extraction method is fused into mean-shift tracker to overcome above difficulties. First, a spatial-temporal saliency feature extraction method is proposed to suppress the interference of the complex background. Furthermore, we proposed a saliency evaluation method by fusing the top-down visual mechanism to enhance the tracking performance. Finally, the efficiency of the saliency features based mean-shift tracker is validated through experimental results and analysis.
会议录	Neural Information Processing. 21st International Conference, ICONIP 2014. Proceedings: LNCS 8834
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12864]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
推荐引用方式 GB/T 7714	Suiwu Zheng,Linshan Liu,Hong Qiao. Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker[C]. 见:Neural Information Processing. 21st International Conference, ICONIP 2014. Kuching, Malaysia. 3-6 Nov. 2014.