Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning

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 l2, 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 l2, 1-norm optimization problem.