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.Full text not available from this repository.
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.
|Keyword(s) / Subject(s):||Appearance model, visual tracking, subspace learning, incremental updating|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||17 Jun 2011 08:21|
|Last Modified:||11 Sep 2013 14:47|
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