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题名: Adaptive Unscented Kalman Filters Applied to Visual Tracking
作者: Ding QC(丁其川) ; Zhao XG(赵新刚) ; Han JD(韩建达)
作者部门: 机器人学研究室
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
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
出版日期: 2011
页码: 6页
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.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8548
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
丁其川; 赵新刚; 韩建达.Adaptive Unscented Kalman Filters Applied to Visual Tracking.见:.2011年中国自动化大会论文集,,2011,6页
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