Robust visual tracking via global context regularized Locality-constrained Linear Coding

Locality-constrained Linear Coding (LLC) based visual tracking can give a better and faster tracking performance than traditional sparse representation based tracking methods. However, the existing LLC based methods often use the anchor points near the target to build the sparse coding dictionary fo...

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Bibliographic Details
Published in:Optik (Stuttgart) Vol. 183; pp. 232 - 240
Main Authors: Kang, Bin, Liang, Dong, Yang, Zhenzhen
Format: Journal Article
Language:English
Published: Elsevier GmbH 01.04.2019
Subjects:
Global context
Locality-constrained Linear Coding
Visual tracking
Visual tracking
Global context
Locality-constrained Linear Coding
ISSN:0030-4026, 1618-1336
Online Access:Get full text
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Summary:Locality-constrained Linear Coding (LLC) based visual tracking can give a better and faster tracking performance than traditional sparse representation based tracking methods. However, the existing LLC based methods often use the anchor points near the target to build the sparse coding dictionary for local sparse coding. It may cause a problem that it is hard to discriminate the difference between the negative and positive anchor points in sparse coding dictionary when facing severe background clutter, illumination change and occlusion. In this paper, we propose a context aware sparse coding method to achieve robust visual tracking. The proposed method can prevent the negative anchor points from disturbing the classifier accuracy because it uses a global context regularizer to constrain the sparse coding value of those negative anchor points that are similar to the positive anchor points. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.
ISSN:0030-4026
1618-1336
DOI:10.1016/j.ijleo.2019.02.025