Visual tracking via incremental Log-Euclidean Riemannian subspace learning

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...

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Vydáno v:2008 IEEE Conference on Computer Vision and Pattern Recognition s. 1 - 8
Hlavní autoři: Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2008
Témata:
Cameras
Covariance matrix
Inference algorithms
Kernel
Lighting
Particle filters
Particle tracking
Principal component analysis
Robustness
Statistics
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
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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.
AbstractList 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.
Author Zhongfei Zhang
Mingliang Zhu
Jian Cheng
Xi Li
Xiaoqin Zhang
Weiming Hu
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  surname: Jian Cheng
  fullname: Jian Cheng
  organization: Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing
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Snippet Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and...
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SubjectTerms Cameras
Covariance matrix
Inference algorithms
Kernel
Lighting
Particle filters
Particle tracking
Principal component analysis
Robustness
Statistics
Title Visual tracking via incremental Log-Euclidean Riemannian subspace learning
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