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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: 2012 IEEE International Conference on Information and Automation, ICIA 2012
会议日期: June 6, 2012 - June 8, 2012
会议地点: Shenyang, China
会议录: 2012 IEEE International Conference on Information and Automation, ICIA 2012
会议录出版者: IEEE Computer Society
会议录出版地: Washington, DC
出版日期: 2012
页码: 491-496
收录类别: CPCI(ISTP) ; EI
ISBN号: 978-1467322386
关键词: Estimation ; Nonlinear filtering ; Tracking (position)
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 (AUKFs), named the MIT-based AUKF and the master-slave-structure AUKF, respectively, are proposed 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. © 2012 IEEE.
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/9864
Appears in Collections:机器人学研究室_会议论文

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