Paper
27 September 2011 An experimental comparison of online object-tracking algorithms
Qing Wang, Feng Chen, Wenli Xu, Ming-Hsuan Yang
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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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qing Wang, Feng Chen, Wenli Xu, and Ming-Hsuan Yang "An experimental comparison of online object-tracking algorithms", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381A (27 September 2011); https://doi.org/10.1117/12.895965
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CITATIONS
Cited by 57 scholarly publications.
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KEYWORDS
Detection and tracking algorithms

Motion models

Optical tracking

Particle filters

Stochastic processes

Visualization

Expectation maximization algorithms

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