The Community for Technology Leaders
2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2242-5
pp: 1-8
Xi Li , National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
Weiming Hu , National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
Zhongfei Zhang , State University of New York, Binghamton, 13902, USA
Xiaoqin Zhang , National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
Mingliang Zhu , National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
Jian Cheng , National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
ABSTRACT
Recently, a novel Log-Euclidean Riemannian metric [28] 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.
INDEX TERMS
CITATION

Weiming Hu, Xiaoqin Zhang, Jian Cheng, Xi Li, Zhongfei Zhang and Mingliang Zhu, "Visual tracking via incremental Log-Euclidean Riemannian subspace learning," 2008 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, AK, USA, 2008, pp. 1-8.
doi:10.1109/CVPR.2008.4587516
96 ms
(Ver 3.3 (11022016))