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Robust Visual Tracking and Vehicle Classification via Sparse Representation
November 2011 (vol. 33 no. 11)
pp. 2259-2272
ASCII Text
x 
 
Xue Mei, Haibin Ling, "Robust Visual Tracking and Vehicle Classification via Sparse Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2259-2272, November, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.66,
author = {Xue Mei and Haibin Ling},
title = {Robust Visual Tracking and Vehicle Classification via Sparse Representation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {11},
issn = {0162-8828},
year = {2011},
pages = {2259-2272},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.66},
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 Visual Tracking and Vehicle Classification via Sparse Representation
IS - 11
SN - 0162-8828
SP2259
EP2272
EPD - 2259-2272
A1 - Xue Mei,
A1 - Haibin Ling,
PY - 2011
KW - Visual tracking
KW - sparse representation
KW - compressive sensing
KW - simultaneous tracking and recognition
KW - particle filter
KW - \ell_1 minimization.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Xue Mei, University of Maryland, Folsom
Haibin Ling, Temple University, Philadelphia
In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an \ell_1-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.

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Index Terms:
Visual tracking, sparse representation, compressive sensing, simultaneous tracking and recognition, particle filter, \ell_1 minimization.
Citation:
Xue Mei, Haibin Ling, "Robust Visual Tracking and Vehicle Classification via Sparse Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2259-2272, Nov. 2011, doi:10.1109/TPAMI.2011.66
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