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.

该论文提出了一种在密集场景中进行实时异常检测和定位的方法，在该方法中，视频被定义为非重叠立方块的集合，并使用两个本地和全局描述符进行描述。我们利用简单和经济实惠的高斯分类器来区分正常活动和异常活动，并根据相邻块之间的结构相似性和无监督学习的特征来确定本地和全局特征。大量实验结果表明，该算法优于 UCSD ped2 和 UMN 基准的最新方法，而且具有更高的时间效率。我们的实验结果证明了我们的系统可以在视频中发生异常时可靠地检测和定位异常。