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Proceedings Paper

An experimental comparison of online object-tracking algorithms
Author(s): Qing Wang; Feng Chen; Wenli Xu; Ming-Hsuan Yang
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Paper Abstract

This paper reviews and evaluates several state-of-the-art online object tracking algorithms. Notwithstanding decades of efforts, object tracking remains a challenging problem due to factors such as illumination, pose, scale, deformation, motion blur, noise, and occlusion. To account for appearance change, most recent tracking algorithms focus on robust object representations and effective state prediction. In this paper, we analyze the components of each tracking method and identify their key roles in dealing with specific challenges, thereby shedding light on how to choose and design algorithms for different situations. We compare state-of-the-art online tracking methods including the IVT,1 VRT,2 FragT,3 BoostT,4 SemiT,5 BeSemiT,6 L1T,7 MILT,8 VTD9 and TLD10 algorithms on numerous challenging sequences, and evaluate them with different performance metrics. The qualitative and quantitative comparative results demonstrate the strength and weakness of these algorithms.

Paper Details

Date Published: 27 September 2011
PDF: 11 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381A (27 September 2011); doi: 10.1117/12.895965
Show Author Affiliations
Qing Wang, Tsinghua Univ. (China)
Feng Chen, Tsinghua Univ. (China)
Wenli Xu, Tsinghua Univ. (China)
Ming-Hsuan Yang, Univ. of California, Merced (United States)


Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)

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