Shreyamsha Kumar, B. K. 
ORCID: https://orcid.org/0000-0002-3781-0635, Swamy, M.N.S. ORCID: https://orcid.org/0000-0002-3989-5476 and Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659
(2018)
Visual Tracking Based on Correlation Filter and Robust Coding in Bilateral 2DPCA Subspace.
IEEE Access, 6
(1).
pp. 73052-73067.
ISSN 2169-3536
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5MBPublished Paper-08537877.pdf - Published Version Available under License Spectrum Terms of Access. |
Official URL: https://doi.org/10.1109/ACCESS.2018.2881723
Abstract
The success of correlation filters in visual tracking has attracted much attention in computer vision due to their high efficiency and performance. However, they are not equipped with a mechanism to cope with challenging situations like scale variations, out-of-view, and camera motion. With the aim of dealing with such situations, a collaborative scheme of tracking based on the discriminative and generative models is proposed. Instead of finding all the affine motion parameters of the target by the combined likelihood of these models, the correlation filters, based on discriminative model, are used to find the position of the target, whereas 2D robust coding in a bilateral 2DPCA subspace, based on generative model, is used to find the other affine motion parameters of the target. Further, a 2D robust coding distance is proposed to differentiate the candidate samples from the subspace and used to compute the observation likelihood in the generative model. In addition, it is proposed to generate a robust occlusion map from the weights obtained during the residual minimization and a novel update mechanism of the appearance model for both the correlation filters and bilateral 2DPCA subspace is proposed. The proposed method is evaluated on the challenging image sequences available in the OTB-50, VOT2016, and UAV20L benchmark datasets, and its performance is compared with that of the state-of-the-art tracking algorithms. In contrast to OTB-50 and VOT2016, the dataset UAV20L contains long duration sequences with additional challenges introduced by both the camera motion and the view points in three dimensions. Quantitative and qualitative performance evaluations on three benchmark datasets demonstrate that the proposed tracking algorithm outperforms the state-of-the-art methods.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
|---|---|
| Item Type: | Article |
| Refereed: | Yes |
| Authors: | Shreyamsha Kumar, B. K. and Swamy, M.N.S. and Ahmad, M. Omair |
| Journal or Publication: | IEEE Access |
| Date: | 19 December 2018 |
| Funders: |
|
| Digital Object Identifier (DOI): | 10.1109/ACCESS.2018.2881723 |
| Keywords: | Visual tracking, weighted least squares, principle component analysis (PCA), bilateral 2DPCA (B2DPCA), occlusion map, correlation filters. |
| ID Code: | 984781 |
| Deposited By: | M. OMAIR AHMAD |
| Deposited On: | 21 Dec 2018 18:07 |
| Last Modified: | 21 Dec 2018 18:07 |
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