Multifeatures Based Compressive Sensing Tracking
Joint Authors
Bo, Yuming
He, Liang
Zhao, Gaopeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures.
In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates.
With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the likelihood which weights particles.
After that, the target template is adaptively updated according to the Bhattacharyya coefficients.
Some experimental results demonstrate that the proposed tracker appears to have better robustness compared with four different algorithms.
American Psychological Association (APA)
He, Liang& Bo, Yuming& Zhao, Gaopeng. 2014. Multifeatures Based Compressive Sensing Tracking. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-472470
Modern Language Association (MLA)
He, Liang…[et al.]. Multifeatures Based Compressive Sensing Tracking. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-472470
American Medical Association (AMA)
He, Liang& Bo, Yuming& Zhao, Gaopeng. Multifeatures Based Compressive Sensing Tracking. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-472470
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-472470