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Robust tensor subspace learning for anomaly detection

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Abstract

Background modeling plays an important role in many applications of computer vision such as anomaly detection and visual tracking. Most existing algorithms for learning appearance model are vector-based methods without maintaining the 2D spatial structure information of objects in an image. To this end, a robust tensor subspace learning algorithm is developed for background modeling which can capture the appearance changes through adaptively updating the tensor subspace. In the tensor framework, the spatial structure information is maintained and utilized for feature extraction of objects. Then by incorporating the robust scheme, we can weight individual pixel of an image to reduce the influence of outliers on background modeling. Furthermore an incremental algorithm for the robust tensor subspace learning is proposed to adapt to the variation of appearance model. The experimental results illustrate the effectiveness of the proposed robust learning algorithm for anomaly detection.

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Acknowledgements

We want to thank the helpful comments and suggestions from the anonymous reviewers. This research was supported partially by the National Natural Science Foundation of China under Grant 60832005; by the Ph.D. Programs Foundation of Ministry of Education of China under Grant 20090203110002; by the Key Science and Technology Program of Shaanxi Province of China under Grant. 2010K06-12; and by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2009JM8004.

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Authors and Affiliations

  1. Video and Image Processing System Lab, School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jie Li, Guan Han, Jing Wen & Xinbo Gao

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  1. Jie Li
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  2. Guan Han
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  3. Jing Wen
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  4. Xinbo Gao
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Correspondence to Xinbo Gao.

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Li, J., Han, G., Wen, J. et al. Robust tensor subspace learning for anomaly detection. Int. J. Mach. Learn. & Cyber. 2, 89–98 (2011). https://doi.org/10.1007/s13042-011-0017-0

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  • DOI: https://doi.org/10.1007/s13042-011-0017-0

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