Title :
Video detection anomaly via low-rank and sparse decompositions
Author :
Lam Tran ; Navasca, C. ; Jiebo Luo
Author_Institution :
Univ. of Alabama at Birmingham, Univ. of Rochester, Rochester, NY, USA
Abstract :
In this paper, we purpose a method for anomaly detection in surveillance video in a tensor framework. We treat a video as a tensor and utilize a stable PCA to decompose it into two tensors, the first tensor is a low rank tensor that consists of background pixels and the second tensor is a sparse tensor that consists of the foreground pixels. The sparse tensor is then analyzed to detect anomaly. The proposed method is a one-shot framework to determine frames that are anomalous in a video.
Keywords :
data mining; principal component analysis; sparse matrices; tensors; video signal processing; video surveillance; background pixels; foreground pixels; low rank tensor; low-rank decompositions; one-shot framework; second tensor; sparse decompositions; sparse tensor; stable PCA; tensor framework; video detection anomalysurveillance video anomaly detection; Educational institutions; Matrix converters; Matrix decomposition; Sparse matrices; Sun; Tensile stress; Vectors; Anomaly detection; Stable PCA; Surveillance Video; Tensor;
Conference_Titel :
Image Processing Workshop (WNYIPW), 2012 Western New York
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-5598-8
DOI :
10.1109/WNYIPW.2012.6466649
