Zhang, X. and Shi, X. and Hu, W. and Li, Xi and Maybank, Stephen J. (2011) Visual tracking via dynamic tensor analysis with mean update. Neurocomputing 74 (17), pp. 3277-3285. ISSN 0925-2312.
Abstract
The appearance model is an important issue in the visual tracking community. Most subspace-based appearance models focus on the time correlation between the image observations of the object, but the spatial layout information of the object is ignored. This paper proposes a robust appearance model for visual tracking which effectively combines the spatial and temporal eigen-spaces of the object in a tensor reconstruction way. In order to capture the variations in object appearance, an incremental updating strategy is developed to both update the eigen-space and mean of the object. Experimental results demonstrate that, compared with the state-of-the-art appearance models in the tracking literature, the proposed appearance model is more robust and effective.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Appearance model, visual tracking, subspace learning, incremental updating |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 17 Jun 2011 08:21 |
| Last Modified: | 09 Aug 2023 12:30 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/3750 |
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