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Grayscale-thermal tracking via inverse sparse representation-based collaborative encoding

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posted on 2020-03-26, 10:23 authored by B Kang, D Liang, W Ding, Huiyu Zhou, W-P Zhu
Grayscale-thermal tracking has attracted a great deal of attention due to its capability of fusing two different yet complementary target observations. Existing methods often consider extracting the discriminative target information and exploring the target correlation among different images as two separate issues, ignoring their interdependence. This may cause tracking drifts in challenging video pairs. This paper presents a collaborative encoding model called joint correlation and discriminant analysis based inver-sparse representation (JCDA-InvSR) to jointly encode the target candidates in the grayscale and thermal video sequences. In particular, we develop a multi-objective programming to integrate the feature selection and the multi-view correlation analysis into a unified optimization problem in JCDA-InvSR, which can simultaneously highlight the special characters of the grayscale and thermal targets through alternately optimizing two aspects: the target discrimination within a given image and the target correlation across different images. For robust grayscale-thermal tracking, we also incorporate the prior knowledge of target candidate codes into the SVM based target classifier to overcome the overfitting caused by limited training labels. Extensive experiments on GTOT and RGBT234 datasets illustrate the promising performance of our tracking framework.

Funding

National Key R & D Programme of China;10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100004608-Natural Science Foundation of Jiangsu Province; 10.13039/5011000012246-Priority Academic Program Development of Jiangsu Higher Education Institutions

History

Citation

IEEE Transactions on Image Processing, 2020, vol. 29, pp. 3401-3415

Author affiliation

Department of Informatics

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Image Processing

Volume

29

Pagination

3401-3415

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1057-7149

eissn

1941-0042

Acceptance date

2019-12-02

Copyright date

2020

Publisher version

https://ieeexplore.ieee.org/document/8941283

Language

en

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