Adaptive Unscented Kalman Filters Applied to Visual Tracking

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	Adaptive Unscented Kalman Filters Applied to Visual Tracking
	Ding QC(丁其川); Zhao XG(赵新刚); Han JD(韩建达)
	2011
会议名称	2011年中国自动化大会暨钱学森诞辰一百周年及中国自动化学会五十周年会庆
会议日期	November 27-29, 2011
会议地点	北京
页码	6页
中文摘要	The classic Bays filters applied to model-based visual tracking suffers from high computation complexity and performance degradation when the inaccurate priori knowledge is involved. In order to improve tracking real-time and accuracy, two kinds of adaptive unscented Kalman filters (AUKF), named the MIT-based AUKF and the master-slave-structure AUKF, respectively, are introduced to estimate the 3-D rigid-body motion from sequential images. The filters use certain feature points’ image coordinates as input data to estimate the position and orientation of the object at each instant when an image is captured, and to recover the velocity and angular velocity of the object between consecutive frames. Experimental results show that both the AUKFs can improve estimation real-time and accuracy in visual tracking.
产权排序	1
会议主办者	中国自动化学
会议录	2011年中国自动化大会论文集
语种	英语
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/8548]
专题	沈阳自动化研究所_机器人学研究室
推荐引用方式 GB/T 7714	Ding QC,Zhao XG,Han JD. Adaptive Unscented Kalman Filters Applied to Visual Tracking[C]. 见:2011年中国自动化大会暨钱学森诞辰一百周年及中国自动化学会五十周年会庆. 北京. November 27-29, 2011.