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https://hdl.handle.net/2440/67304
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Type: | Conference paper |
Title: | Visual tracking via incremental Log-Euclidean Riemannian subspace learning |
Author: | Li, X. Hu, W. Zhang, Z. Zhang, X. Zhu, M. Cheng, J. |
Citation: | IEEE International Conference on Computer Vision and Pattern Recognition 2008 / pp.1-8 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2008 |
ISBN: | 9781424422425 |
Conference Name: | IEEE International Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska) |
Statement of Responsibility: | Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng |
Abstract: | Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework. |
Rights: | ©2008 IEEE |
DOI: | 10.1109/CVPR.2008.4587516 |
Published version: | http://dx.doi.org/10.1109/cvpr.2008.4587516 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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