Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 2, Issue 2, Pages 89-98Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-011-0017-0
Keywords
Background modeling; Tensor subspace; Robust learning; Incremental learning; Anomaly detection
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Funding
- National Natural Science Foundation of China [60832005]
- Ph.D. Programs Foundation of Ministry of Education of China [20090203110002]
- Key Science and Technology Program of Shaanxi Province of China [2010K06-12]
- Natural Science Basic Research Plan in Shaanxi Province of China [2009JM8004]
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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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