Online multi-object tracking via labeled random finite set with appearance learning
© 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false posi...
| Main Author: | |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/66632 |
| _version_ | 1848761357019643904 |
|---|---|
| author | Kim, Du Yong |
| author_facet | Kim, Du Yong |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for the purpose of learning an object's appearance model, and uses this additional information in the construction an augmented likelihood which improves performance and facilitates track re-initialization. This approach enhances the baseline tracking algorithm and shows better performance with respect to mis-detections, occlusions and false track rejection. Competitive tracking results are shown compared to state-of-the-art algorithms on PETS benchmark [1] video datasets. |
| first_indexed | 2025-11-14T10:30:23Z |
| format | Conference Paper |
| id | curtin-20.500.11937-66632 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:30:23Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-666322018-05-14T06:17:52Z Online multi-object tracking via labeled random finite set with appearance learning Kim, Du Yong © 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for the purpose of learning an object's appearance model, and uses this additional information in the construction an augmented likelihood which improves performance and facilitates track re-initialization. This approach enhances the baseline tracking algorithm and shows better performance with respect to mis-detections, occlusions and false track rejection. Competitive tracking results are shown compared to state-of-the-art algorithms on PETS benchmark [1] video datasets. 2017 Conference Paper http://hdl.handle.net/20.500.11937/66632 10.1109/ICCAIS.2017.8217572 restricted |
| spellingShingle | Kim, Du Yong Online multi-object tracking via labeled random finite set with appearance learning |
| title | Online multi-object tracking via labeled random finite set with appearance learning |
| title_full | Online multi-object tracking via labeled random finite set with appearance learning |
| title_fullStr | Online multi-object tracking via labeled random finite set with appearance learning |
| title_full_unstemmed | Online multi-object tracking via labeled random finite set with appearance learning |
| title_short | Online multi-object tracking via labeled random finite set with appearance learning |
| title_sort | online multi-object tracking via labeled random finite set with appearance learning |
| url | http://hdl.handle.net/20.500.11937/66632 |