Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking
In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
IEEE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/56045 |
| _version_ | 1848759772173565952 |
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| author | Kim, Du Yong Jeon, M. |
| author_facet | Kim, Du Yong Jeon, M. |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with l 1 -norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker. © 1992-2012 IEEE. |
| first_indexed | 2025-11-14T10:05:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-56045 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:11Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-560452023-08-02T06:39:09Z Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking Kim, Du Yong Jeon, M. In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with l 1 -norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker. © 1992-2012 IEEE. 2013 Journal Article http://hdl.handle.net/20.500.11937/56045 10.1109/TIP.2012.2218824 IEEE restricted |
| spellingShingle | Kim, Du Yong Jeon, M. Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title | Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title_full | Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title_fullStr | Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title_full_unstemmed | Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title_short | Spatio-temporal auxiliary particle filtering with l1-Norm- Based Appearance Model Learning for Robust Visual Tracking |
| title_sort | spatio-temporal auxiliary particle filtering with l1-norm- based appearance model learning for robust visual tracking |
| url | http://hdl.handle.net/20.500.11937/56045 |