Survey of single‐target visual tracking methods based on online learning
Visual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2013.0134 |
| _version_ | 1797685174826172416 |
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| author | Qi Liu Xiaoguang Zhao Zengguang Hou |
| author_facet | Qi Liu Xiaoguang Zhao Zengguang Hou |
| author_sort | Qi Liu |
| collection | DOAJ |
| description | Visual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state‐of‐the‐art online‐learning‐based tracking methods by category. We provide detail descriptions of representative methods in each category, and examine their pros and cons. Moreover, several most representative algorithms are implemented to provide quantitative reference. At last, we outline several trends for future visual tracking research. |
| first_indexed | 2024-03-12T00:41:15Z |
| format | Article |
| id | doaj.art-72b2e19af5a44ed1a7803fc49f6158cf |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:41:15Z |
| publishDate | 2014-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-72b2e19af5a44ed1a7803fc49f6158cf2023-09-15T07:16:00ZengWileyIET Computer Vision1751-96321751-96402014-10-018541942810.1049/iet-cvi.2013.0134Survey of single‐target visual tracking methods based on online learningQi Liu0Xiaoguang Zhao1Zengguang Hou2The State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijing100080People's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijing100080People's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijing100080People's Republic of ChinaVisual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state‐of‐the‐art online‐learning‐based tracking methods by category. We provide detail descriptions of representative methods in each category, and examine their pros and cons. Moreover, several most representative algorithms are implemented to provide quantitative reference. At last, we outline several trends for future visual tracking research.https://doi.org/10.1049/iet-cvi.2013.0134single-target visual tracking methodcomputer visionroboticstarget appearanceonline learning schemeadvanced visual tracking framework |
| spellingShingle | Qi Liu Xiaoguang Zhao Zengguang Hou Survey of single‐target visual tracking methods based on online learning IET Computer Vision single-target visual tracking method computer vision robotics target appearance online learning scheme advanced visual tracking framework |
| title | Survey of single‐target visual tracking methods based on online learning |
| title_full | Survey of single‐target visual tracking methods based on online learning |
| title_fullStr | Survey of single‐target visual tracking methods based on online learning |
| title_full_unstemmed | Survey of single‐target visual tracking methods based on online learning |
| title_short | Survey of single‐target visual tracking methods based on online learning |
| title_sort | survey of single target visual tracking methods based on online learning |
| topic | single-target visual tracking method computer vision robotics target appearance online learning scheme advanced visual tracking framework |
| url | https://doi.org/10.1049/iet-cvi.2013.0134 |
| work_keys_str_mv | AT qiliu surveyofsingletargetvisualtrackingmethodsbasedononlinelearning AT xiaoguangzhao surveyofsingletargetvisualtrackingmethodsbasedononlinelearning AT zengguanghou surveyofsingletargetvisualtrackingmethodsbasedononlinelearning |