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Robust Object Tracking with Online Multiple Instance Learning
August 2011 (vol. 33 no. 8)
pp. 1619-1632
ASCII Text
x 
 
Boris Babenko, Ming-Hsuan Yang, Serge Belongie, "Robust Object Tracking with Online Multiple Instance Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619-1632, August, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2010.226,
author = {Boris Babenko and Ming-Hsuan Yang and Serge Belongie},
title = {Robust Object Tracking with Online Multiple Instance Learning},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {8},
issn = {0162-8828},
year = {2011},
pages = {1619-1632},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.226},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Robust Object Tracking with Online Multiple Instance Learning
IS - 8
SN - 0162-8828
SP1619
EP1632
EPD - 1619-1632
A1 - Boris Babenko,
A1 - Ming-Hsuan Yang,
A1 - Serge Belongie,
PY - 2011
KW - Visual Tracking
KW - multiple instance learning
KW - online boosting.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Boris Babenko, University of California, San Diego, La Jolla
Ming-Hsuan Yang, University of California, Merced, Merced
Serge Belongie, University of California, San Diego, La Jolla
In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.

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Index Terms:
Visual Tracking, multiple instance learning, online boosting.
Citation:
Boris Babenko, Ming-Hsuan Yang, Serge Belongie, "Robust Object Tracking with Online Multiple Instance Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619-1632, Aug. 2011, doi:10.1109/TPAMI.2010.226
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