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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.

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Official URL: http://dx.doi.org/10.1016/j.neucom.2011.05.006

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.

Item Type: Article
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
Depositing User: Administrator
Date Deposited: 17 Jun 2011 08:21
Last Modified: 11 Sep 2013 14:47
URI: http://eprints.bbk.ac.uk/id/eprint/3750

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