Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/70396
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Type: | Conference paper |
Title: | Graph mode-based contextual kernels for robust SVM tracking |
Author: | Li, X. Dick, A. Wang, H. Shen, C. Van Den Hengel, A. |
Citation: | 2011 IEEE International Conference on Computer Vision, 2011: pp.1156-1163 |
Publisher: | IEEE |
Publisher Place: | USA |
Issue Date: | 2011 |
Series/Report no.: | IEEE International Conference on Computer Vision |
ISBN: | 9781457711015 |
ISSN: | 1550-5499 |
Conference Name: | International Conference on Computer Vision (13th : 2011 : Barcelona, Spain) |
Statement of Responsibility: | Xi Li, Anthony Dick, Hanzi Wang, Chunhua Shen, Anton van den Hengel |
Abstract: | Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to complicated factors such as noises, outliers, background clutters and so on. In this paper, we propose a visual tracker based on support vector machines (SVMs), for which a novel graph mode-based contextual kernel is designed to effectively capture the higher-order contextual information from samples. To do so, we first create a visual graph whose similarity matrix is determined by a baseline visual kernel. Second, a set of high-order contexts are discovered in the visual graph. The problem of discovering these high-order contexts is solved by seeking modes of the visual graph. Each graph mode corresponds to a vertex community termed as a high-order context. Third, we construct a contextual kernel that effectively captures the interaction information between the high-order contexts. Finally, this contextual kernel is embedded into SVMs for robust tracking. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker. |
Rights: | Copyright © 2011 by IEEE. |
DOI: | 10.1109/ICCV.2011.6126364 |
Description (link): | http://www.iccv2011.org/ |
Published version: | http://dx.doi.org/10.1109/iccv.2011.6126364 |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
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RA_hdl_70396.pdf Restricted Access | Restricted Access | 3.33 MB | Adobe PDF | View/Open |
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