Robust tensor subspace learning for anomaly detection
Robust tensor subspace learning for anomaly detection
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DOI：
10.1007/s13042-011-0017-0
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发表时间：
2011-03
影响因子：
5.6
通讯作者：
Jie Li;Guan Han;Jing Wen;Xinbo Gao
中科院分区：
文献类型：
--
作者：
Jie Li;Guan Han;Jing Wen;Xinbo Gao
背景建模在异常检测、视觉跟踪等计算机视觉应用中起着重要的作用。现有的大多数外观模型学习算法都是基于矢量的方法，没有维护图像中物体的二维空间结构信息。为此，提出了一种鲁棒的张量子空间学习算法，通过自适应更新张量子空间来捕捉图像的外观变化。在张量框架中，空间结构信息被保持并用于对象的特征提取。然后，通过结合鲁棒方案，我们可以加权图像的单个像素，以减少离群点对背景建模的影响。此外，为了适应外观模型的变化，提出了一种增量式张量子空间学习算法。实验结果证明了所提出的鲁棒学习算法用于异常检测的有效性。
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.