Robust mean-shift tracker with local saliency feature and spatial pattern preserved metric

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

	Robust mean-shift tracker with local saliency feature and spatial pattern preserved metric
	Linshan Liu; Suiwu Zheng; Hong Qiao; Min Wang
	2013
会议名称	2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)
会议日期	20-22 Dec. 2013
会议地点	Shengyang, China
关键词	NONE
通讯作者	Linshan Liu
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, we present an improved mean-shift tracker which uses discriminative localsaliency feature and a new spatial pattern preserved similarity metric method to overcome above difficulties in mean-shift based tracking approaches. The local saliency feature, which is composed of contrast color, texture and gradient around the target, is proposed to find the most distinguished features between the target and background, and it could enhance the tracking performance greatly in the cluttered and complex environment. Another important benefit of this feature is that the saliencymap form could be easily embedded into the mean-shift framework. The new similarity metric try to preserve the spatial pattern to reduce the similarity errors caused by different spatial structure. It is beneficial to the mean-shift tracker to face the targets and scenes which has identical texture and colorfeature and with different spatial patterns. Finally, the efficiency of the proposed improved mean-shifttracker is validated through the plenty experimental results and analysis.
会议录	Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12870]
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	Linshan Liu,Suiwu Zheng,Hong Qiao,et al. Robust mean-shift tracker with local saliency feature and spatial pattern preserved metric[C]. 见:2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). Shengyang, China. 20-22 Dec. 2013.