Visual tracking via dynamic tensor analysis with mean update
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 |
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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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