Robust object tracking algorithm based on sparse eigenbasis
To reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low‐dimensional sub‐space representation...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-12-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2013.0175 |
| _version_ | 1797684531081248768 |
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| author | Jing Li Junzheng Wang |
| author_facet | Jing Li Junzheng Wang |
| author_sort | Jing Li |
| collection | DOAJ |
| description | To reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low‐dimensional sub‐space representation based on Karhunen–Loeve transform learned online. Meanwhile, combining the Bayesian inference, an adaptive object tracker is presented. First, the authors represent the appearance of the object in a low‐dimensional sub‐space, then the authors obtain the optimal estimation of the state parameters by Bayesian inference. Finally, the authors update the eigenbasis space using the optimal observations. Experimental results show that the proposed method is able to track the objects effectively and robustly under temporary occlusion and large illumination changes. |
| first_indexed | 2024-03-12T00:31:02Z |
| format | Article |
| id | doaj.art-36b89adf10794a95a931b35370b79422 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:31:02Z |
| publishDate | 2014-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-36b89adf10794a95a931b35370b794222023-09-15T10:15:58ZengWileyIET Computer Vision1751-96321751-96402014-12-018660161010.1049/iet-cvi.2013.0175Robust object tracking algorithm based on sparse eigenbasisJing Li0Junzheng Wang1Key Laboratory of Complex System Intelligent Control and DecisionSchool of AutomationBeijing Institute of TechnologyBeijing100081People's Republic of ChinaKey Laboratory of Complex System Intelligent Control and DecisionSchool of AutomationBeijing Institute of TechnologyBeijing100081People's Republic of ChinaTo reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low‐dimensional sub‐space representation based on Karhunen–Loeve transform learned online. Meanwhile, combining the Bayesian inference, an adaptive object tracker is presented. First, the authors represent the appearance of the object in a low‐dimensional sub‐space, then the authors obtain the optimal estimation of the state parameters by Bayesian inference. Finally, the authors update the eigenbasis space using the optimal observations. Experimental results show that the proposed method is able to track the objects effectively and robustly under temporary occlusion and large illumination changes.https://doi.org/10.1049/iet-cvi.2013.0175robust object tracking algorithmsparse eigenbasiscomputation reductionperformance improvementobject detection algorithmobject appearance variation |
| spellingShingle | Jing Li Junzheng Wang Robust object tracking algorithm based on sparse eigenbasis IET Computer Vision robust object tracking algorithm sparse eigenbasis computation reduction performance improvement object detection algorithm object appearance variation |
| title | Robust object tracking algorithm based on sparse eigenbasis |
| title_full | Robust object tracking algorithm based on sparse eigenbasis |
| title_fullStr | Robust object tracking algorithm based on sparse eigenbasis |
| title_full_unstemmed | Robust object tracking algorithm based on sparse eigenbasis |
| title_short | Robust object tracking algorithm based on sparse eigenbasis |
| title_sort | robust object tracking algorithm based on sparse eigenbasis |
| topic | robust object tracking algorithm sparse eigenbasis computation reduction performance improvement object detection algorithm object appearance variation |
| url | https://doi.org/10.1049/iet-cvi.2013.0175 |
| work_keys_str_mv | AT jingli robustobjecttrackingalgorithmbasedonsparseeigenbasis AT junzhengwang robustobjecttrackingalgorithmbasedonsparseeigenbasis |