Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/108759
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Type: | Conference paper |
Title: | Long-term correlation tracking |
Author: | Ma, C. Yang, X. Zhang, C. Yang, M.-H. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, pp.5388-5396 |
Publisher: | IEEE |
Issue Date: | 2015 |
ISBN: | 9781467369657 |
ISSN: | 1063-6919 1063-6919 |
Conference Name: | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (7 Jun 2015 - 12 Jun 2015 : Boston, MA) |
Statement of Responsibility: | Chao Ma, Xiaokang Yang, Chongyang Zhang, and Ming-Hsuan Yang |
Abstract: | In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness. |
Keywords: | Target tracking; correlation; context; detectors; context modeling; estimation |
Rights: | © 2015 IEEE |
DOI: | 10.1109/CVPR.2015.7299177 |
Published version: | http://dx.doi.org/10.1109/cvpr.2015.7299177 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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RA_hdl_108759.pdf Restricted Access | Restricted Access | 3.05 MB | Adobe PDF | View/Open |
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