Robust Visual Tracking with Improved Subspace Representation Model
| Main Authors: | Cheng, Jing; Jiangnan University, Kang, Sucheng; Yancheng Teachers University |
|---|---|
| Other Authors: | National Natural Science Foundation of China, China Postdoctoral Science Foundation, Technology Research Project of the Ministry of Public Security of China |
| Format: | Article info eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2017
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| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4629 |
Daftar Isi:
- This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity indexIn this paper, we propose a robust visual tracking with an improved subspace representation model. Different from traditional subspace representation model, we use sparse representation, but not the collaborative representation to reconstruct the observation samples, which can avoid the redundant object features in subspace effectively. Moreover, to reject the outliers in the process of tracking, we also propose the combination of sparse box templates and Laplacian residual. To solve the minimization problem of object representation efficiently, a fast numerical algorithm that accelerated proximal gradient (APG) approach is proposed for the Lagrangian function. Finally, experimental results on several challenging video sequences show better performance than LSST and many state-of-the-art trackers.