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Type: Conference paper
Title: Robust Visual Tracking Based on an Effective Appearance Model
Author: Li, X.
Hu, W.
Zhang, Z.
Zhang, X.
Citation: Computer Vision - ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV / D. Forsyth, P. Torr, A. Zisserman (eds.), pp. pp.396-408
Publisher: Springer
Publisher Place: Berlin
Issue Date: 2008
ISBN: 9783540886921
ISSN: 0302-9743
Conference Name: European Conference on Computer Vision (10th : 2008 : Marseille, France)
Statement of
Xi Li, Weiming Hu, Zhongfei Zhang and Xiaoqin Zhang
Abstract: Most existing appearance models for visual tracking usually construct a pixel-based representation of object appearance so that they are incapable of fully capturing both global and local spatial layout information of object appearance. In order to address this problem, we propose a novel spatial Log-Euclidean appearance model (referred as SLAM) under the recently introduced Log-Euclidean Riemannian metric [23]. SLAM is capable of capturing both the global and local spatial layout information of object appearance by constructing a block-based Log-Euclidean eigenspace representation. Specifically, the process of learning the proposed SLAM consists of five steps—appearance block division, online Log-Euclidean eigenspace learning, local spatial weighting, global spatial weighting, and likelihood evaluation. Furthermore, a novel online Log-Euclidean Riemannian subspace learning algorithm (IRSL) [14] is applied to incrementally update the proposed SLAM. 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 SLAM.
Description: Also published as a journal article: Lecture Notes in Computer Science, 2008; 5305(2008):396-408
Rights: © Springer-Verlag Berlin Heidelberg 2008
DOI: 10.1007/978-3-540-88693-8_29
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