Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo sinh học: " Research Article Covariance Tracking via Geometric Particle Filtering"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Covariance Tracking via Geometric Particle Filtering | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 583918 9 pages doi 10.1155 2010 583918 Research Article Covariance Tracking via Geometric Particle Filtering Yunpeng Liu 1 2 3 4 Guangwei Li 5 and Zelin Shi1 3 1Shenyang Institute ofAutomation Chinese Academy of Sciences Shenyang 110016 China 2 Graduate School of Chinese Academy of Sciences Beijing 100049 China 3 Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China 4 Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China 5Management Science and Engineering Department Qingdao University Qingdao 266071 China Correspondence should be addressed to Yunpeng Liu ypliu@sia.cn Received 30 November 2009 Accepted 24 June 2010 Academic Editor Yingzi Du Copyright 2010 Yunpeng Liu et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest which has been applied to visual tracking. We develop a geometric method for visual tracking in which region covariance is used to model objects appearance then tracking is led by implementing the particle filter with the constraint that the system state lies in a low dimensional manifold affine Lie group. The sequential Bayesian updating consists of drawing state samples while moving on the manifold geodesics the region covariance is updated using a novel approach in a Riemannian space. Our main contribution is developing a general particle filtering-based racking algorithm that explicitly take the geometry of affine Lie groups into consideration in deriving the state equation on Lie groups. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of