Abnormal crowd behavior detection is a hot research topic in the field of computer vision. In order to solve the problems of high computational cost and the imbalance between positive and negative samples, we propose an efficient algorithm that can detect and locate anomalies in videos. In order to solve the problem of less negative samples, the algorithm uses the spatiotemporal autoencoder to identify and extract the negative samples (contain abnormal behaviors) in the dataset in an unsupervised learning method. On this basis, a spatiotemporal convolutional neural network (CNN) is constructed with simple structure and low computational complexity. The supervised training method is used to train the spatiotemporal CNN with positive and negative samples to generate the detection model. Experiments are conducted on the UCSD and UMN datasets. The experiment results show that the proposed algorithm can detect and locate abnormal behaviors in real time (using only CPU), and the accuracy of the algorithm exceeds those of the existing algorithms at both the pixel level and frame level.

异常人群行为检测是计算机视觉领域的研究热点。为了解决高计算量和正负样本不平衡的问题，我们提出了一种可以检测和定位视频异常的有效算法。为了解决负样本较少的问题，该算法使用时空自动编码器以无监督学习方法识别和提取数据集中的负样本（包含异常行为）。在此基础上，构建了结构简单，计算复杂度低的时空卷积神经网络。采用监督训练方法对正负样本进行时空CNN训练，生成检测模型。在UCSD和UMN数据集上进行了实验。