Imperial College London
Alternate

ProfessorKrystianMikolajczyk

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Computer Vision and Machine Learning
 
 
 
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Contact

 

+44 (0)20 7594 6220k.mikolajczyk

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Akin:2014:10.1109/ICPR.2014.725,
author = {Akin, O and Mikolajczyk, K},
doi = {10.1109/ICPR.2014.725},
pages = {4229--4233},
title = {Online learning and detection with part-based, circulant structure},
url = {http://dx.doi.org/10.1109/ICPR.2014.725},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - © 2014 IEEE. Circulant Structure Kernel (CSK) has recently been introduced as a simple and extremely efficient tracking method. In this paper, we propose an extension of CSK that explicitly addresses partial occlusion problems which the original CSK suffers from. Our extension is based on a part-based scheme, which improves the robustness and localisation accuracy. Furthermore, we improve the robustness of CSK for long-term tracking by incorporating it into an online learning and detection framework. We provide an extensive comparison to eight recently introduced tracking methods. Our experimental results show that the proposed approach significantly improves the original CSK and provides state-of-the-art results when combined with online learning approach.
AU  - Akin,O
AU  - Mikolajczyk,K
DO  - 10.1109/ICPR.2014.725
EP  - 4233
PY  - 2014///
SN  - 1051-4651
SP  - 4229
TI  - Online learning and detection with part-based, circulant structure
UR  - http://dx.doi.org/10.1109/ICPR.2014.725
ER  -
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