Video Anomaly Detection with Hyperbolic Graph Embedding and Masked Normalizing Flows

Abstract

Video anomaly detection plays a vital role in intelligent video monitoring systems. It has found extensive applications in the fields of public safety and social security. Nevertheless, the area of video anomaly detection continues to be a formidable task because of the intricate nature of actual data and the challenge of precisely identifying anomalies. Current anomaly detection approaches suffer from overpowered generalization ability, weak spatio-temporal feature extraction capability, and insufficient global information aggregation ability. Therefore, an unsupervised Hyperbolic Graph-based Normalizing Flows (HGNF) model is proposed in this paper, which is constructed with a Spatio-temporal Encoder (STEncoder) and stacked normalizing flows to reduce the overgeneralization of auto-encoder-based anomaly detection models. STEncoder consists of spatio-temporal attention and inter-frame feature aggregation. In normalizing flows, a Poincaré ball graph extractor is developed to improve the representation ability of the dynamic changes of the input data, and a masked affine coupling block is established to improve the performance of this model in global information aggregation. According to the experimental results obtained on four public datasets, HGNF achieves excellent performance and the best AUC (Area Under the Curve) score of 74.5% on the UBnormal dataset.