Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning. (December 2020)

• Record Type: Journal Article
• Authors: Li, Ang; Miao, Zhenjiang; Cen, Yigang; Zhang, Xiao-Ping; Zhang, Linna; Chen, Shiming
• Is Part Of: Pattern recognition. Volume 108(2020:Dec.)
• Publication Date: 2020-12
• Subjects: LRCCDL -- Reconstruction cost -- Abnormal event detection -- Crowded scenes -- Surveillance videos; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107355
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: To remove the low variations and noise of objects in the background, we extract the motion descriptor of the foreground by integrating background subtraction with binarization of surveillance videos. In the training stage, to obtain a low-rank dictionary based on the similarity of normal training samples and a compact cluster of reconstruction coefficient vectors surrounding a center in the meantime, we propose a new joint optimization of the nuclear-norm and l 2, 1 -norm. In the detection stage, to obtain a large gap between the reconstruction errors of abnormal testing samples and those of normal testing samples, we force the reconstruction coefficient vectors of abnormal frames to distribute so that they resemble those of normal ones by solving an l 2, 1 -norm optimization problem. Abstract: In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l 2, 1 -norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l 2, 1 -norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l 2, 1 -norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results.