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

	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
会议录名称	Neural Information Processing. 21st International Conference, ICONIP 2014. Proceedings: LNCS 8834
会议日期	3-6 Nov. 2014
会议地点	Kuching, Malaysia
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.
关键词	None
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12864
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	Suiwu Zheng
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],2014.