- Title
- Online dual dictionary learning for visual object tracking
- Creator
- Cheng, Xu; Zhang, Yifeng; Zhou, Lin; Lu, Guojun
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179019
- Identifier
- vital:15497
- Identifier
-
https://doi.org/10.1007/s12652-020-02799-x
- Identifier
- ISBN:1868-5137 (ISSN)
- Abstract
- Sparse representation method has been widely applied to visual tracking. Most of existing tracking algorithms based on sparse representation exploit the l0 or l1-norm for solving the sparse coefficients. However, it makes the execution of solution very time consuming. In this paper, we propose an effective dual dictionary learning model for visual tracking. The dictionary model is composed of discriminative dictionary and analytic dictionary; they work together to perform the representation and discrimination simultaneously. First, we exploit the object states of the first ten frames of a video to initialize the dual dictionary. In the tracking phase, the dual dictionary model is updated alternatively. Second, the local and global information of the object are integrated into the dual dictionary learning model. Sparse coefficients of the patch are used to encode the local structural information of the object. Furthermore, all the sparse coefficients within one object state form a global object representation. We develop a likelihood function that takes an adaptive threshold into consideration to de-noise the global representation. In addition, the object template is updated via an online scheme to adapt the object appearance changes. The experiments on a number of common benchmark test sets show that our approach is more effective than the existing methods. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Journal of Ambient Intelligence and Humanized Computing Vol. 12, no. 12 (2021), p. 10881-10896
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
- Subject
- 0801 Artificial Intelligence and Image Processing; 0805 Distributed Computing; Appearance update; Dictionary learning; Sparse representation; Surveillance; Visual tracking
- Reviewed
- Funder
- This work is supported in part by the National Natural Science Foundation of China (Grant no. 61802058); in part by the International Cooperation and Exchange of the National Natural Science Foundation of China (Grant no. 61911530397); in part by the Equipment Advance Research Foundation Project of China (Grant no. 61403120106); in part by the China Postdoctoral Science Foundation (Grant 2019M651650); in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (Grant no. 2018r057); in part by the Open Project Program of the State Key Lab of CAD&CG (Grant no. A1919), Zhejiang University, and the PAPD fund. Natural Science Foundation of Jiangsu Province (Grant no. BK20201267).
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