  In this paper, we propose a method for real-time anomaly detection and
localization in crowded scenes. Each video is defined as a set of
non-overlapping cubic patches, and is described using two local and global
descriptors. These descriptors capture the video properties from different
aspects. By incorporating simple and cost-effective Gaussian classifiers, we
can distinguish normal activities and anomalies in videos. The local and global
features are based on structure similarity between adjacent patches and the
features learned in an unsupervised way, using a sparse auto- encoder.
Experimental results show that our algorithm is comparable to a
state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more
time-efficient. The experiments confirm that our system can reliably detect and
localize anomalies as soon as they happen in a video.
