Object Tracking via 2DPCA and l2-Regularization
We present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l2-regula...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/7975951 |
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| _version_ | 1832553932579143680 |
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| author | Haijun Wang Hongjuan Ge Shengyan Zhang |
| author_facet | Haijun Wang Hongjuan Ge Shengyan Zhang |
| author_sort | Haijun Wang |
| collection | DOAJ |
| description | We present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms. |
| format | Article |
| id | doaj-art-2840d362c26942dc9044aef43fe04c2b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-2840d362c26942dc9044aef43fe04c2b2025-02-03T05:52:47ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552016-01-01201610.1155/2016/79759517975951Object Tracking via 2DPCA and l2-RegularizationHaijun Wang0Hongjuan Ge1Shengyan Zhang2College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaAviation Information Technology R & D Center, Binzhou University, Binzhou 256603, ChinaWe present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.http://dx.doi.org/10.1155/2016/7975951 |
| spellingShingle | Haijun Wang Hongjuan Ge Shengyan Zhang Object Tracking via 2DPCA and l2-Regularization Journal of Electrical and Computer Engineering |
| title | Object Tracking via 2DPCA and l2-Regularization |
| title_full | Object Tracking via 2DPCA and l2-Regularization |
| title_fullStr | Object Tracking via 2DPCA and l2-Regularization |
| title_full_unstemmed | Object Tracking via 2DPCA and l2-Regularization |
| title_short | Object Tracking via 2DPCA and l2-Regularization |
| title_sort | object tracking via 2dpca and l2 regularization |
| url | http://dx.doi.org/10.1155/2016/7975951 |
| work_keys_str_mv | AT haijunwang objecttrackingvia2dpcaandl2regularization AT hongjuange objecttrackingvia2dpcaandl2regularization AT shengyanzhang objecttrackingvia2dpcaandl2regularization |