Robust tensor subspace learning for anomaly detection
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ORIGINAL ARTICLE
Robust tensor subspace learning for anomaly detection Jie Li • Guan Han • Jing Wen • Xinbo Gao
Received: 28 November 2010 / Accepted: 1 March 2011 / Published online: 12 March 2011 Ó Springer-Verlag 2011
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. Keywords Background modeling Tensor subspace Robust learning Incremental learning Anomaly detection
1 Introduction Anomaly detection in video surveillance by using stationary cameras to monitor an environment of interest has gained much more attention to public security, due to the increasing societal threats from terrorists and crime, which
J. Li G. Han J. Wen X. Gao (&) Video and Image Processing System Lab, School of Electronic Engineering, Xidian University, Xi’an 710071, China e-mail: [email protected]; [email protected]
can be accomplished by learning background model representing normal state and identifying image regions that anomalous with respect to that background model. In this paper, we focus on detecting drastic changes of the ‘‘normal’’ background model. It is suitable for a wide variety of scenarios, such as outdoor parking lots, airport lounge, market hall entrance and etc. However, the main challenge of background representation can be attributed to handling the appearance variation of the scene over time. Illumination changes and camera shaking are regarded as extrinsic appearance variation, whereas intrinsic changes are resulted from object motion and pose variation in the scene [5]. Therefore, robust modeling such appearance variation is of great importance to anomaly detection. In recent years, many works have been done in background modeling. Oliver et al. [4] firstly proposed eigenbackground modeling by preforming principal component analysis (PCA) method. The background model can be represented by the mean image and linear combination of the first p significant eigenvectors. However, the traditional PCA is sensitive to outliers, which can be absorbed into the background model. To enhance the robustness of PCA model, Xu and Yuille [14] introduced a binary variable to
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