Visual multiple-object tracking for unknown clutter rate
© The Institution of Engineering and Technology 2018. In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views....
| Main Author: | |
|---|---|
| Format: | Journal Article |
| Published: |
2018
|
| Online Access: | http://hdl.handle.net/20.500.11937/73041 |
| _version_ | 1848762908374204416 |
|---|---|
| author | Kim, Du Yong |
| author_facet | Kim, Du Yong |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © The Institution of Engineering and Technology 2018. In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this study, the authors are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalised labelled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios. |
| first_indexed | 2025-11-14T10:55:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-73041 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:55:02Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-730412018-12-13T09:33:39Z Visual multiple-object tracking for unknown clutter rate Kim, Du Yong © The Institution of Engineering and Technology 2018. In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this study, the authors are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalised labelled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios. 2018 Journal Article http://hdl.handle.net/20.500.11937/73041 10.1049/iet-cvi.2017.0600 restricted |
| spellingShingle | Kim, Du Yong Visual multiple-object tracking for unknown clutter rate |
| title | Visual multiple-object tracking for unknown clutter rate |
| title_full | Visual multiple-object tracking for unknown clutter rate |
| title_fullStr | Visual multiple-object tracking for unknown clutter rate |
| title_full_unstemmed | Visual multiple-object tracking for unknown clutter rate |
| title_short | Visual multiple-object tracking for unknown clutter rate |
| title_sort | visual multiple-object tracking for unknown clutter rate |
| url | http://hdl.handle.net/20.500.11937/73041 |