ORIGINAL ARTICLE Robust tensor subspace learning for anomaly detection

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 adap-tively updating the tensor subspace. In the tensor frame-work, the spatial structure information is maintained and utilized for feature extraction of objects. Then by incor-porating the robust scheme, we can weight individual pixel of an image to reduce the influence of outliers on back-ground 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