Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-18
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Object tracking based on sparse representation has given promising tracking results in recent years.
However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information.
In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image.
In this paper, we propose a robust tracking algorithm.
Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously.
A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance.
Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models.
Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results.
Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
American Psychological Association (APA)
Yang, Honghong& Shi-ru, Qu. 2016. Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099712
Modern Language Association (MLA)
Yang, Honghong& Shi-ru, Qu. Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1099712
American Medical Association (AMA)
Yang, Honghong& Shi-ru, Qu. Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099712
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099712