Robust auxiliary particle filter with an adaptive appearance model for visual tracking
The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filter...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/55342 |
| _version_ | 1848759596385042432 |
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| author | Kim, Du Yong Yang, E. Jeon, M. Shin, V. |
| author_facet | Kim, Du Yong Yang, E. Jeon, M. Shin, V. |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined. © 2011 Springer-Verlag Berlin Heidelberg. |
| first_indexed | 2025-11-14T10:02:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55342 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:02:24Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-553422017-09-13T16:10:06Z Robust auxiliary particle filter with an adaptive appearance model for visual tracking Kim, Du Yong Yang, E. Jeon, M. Shin, V. The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined. © 2011 Springer-Verlag Berlin Heidelberg. 2011 Conference Paper http://hdl.handle.net/20.500.11937/55342 10.1007/978-3-642-19318-7_56 restricted |
| spellingShingle | Kim, Du Yong Yang, E. Jeon, M. Shin, V. Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title | Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title_full | Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title_fullStr | Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title_full_unstemmed | Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title_short | Robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| title_sort | robust auxiliary particle filter with an adaptive appearance model for visual tracking |
| url | http://hdl.handle.net/20.500.11937/55342 |