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An experimental comparison of online object-tracking algorithmsFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
PDF: 11 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381A (27 September 2011); doi: 10.1117/12.895965
Show Author Affiliations
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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