Robust Video Anomaly Detection Framework via Prior Knowledge and Multi-Path Frame Prediction

Video anomaly detection aims to automatically detect abnormal objects or behaviors. Most existing methods tackle the problem by minimizing the reconstruction errors stemming from the lack of anomalous data, which leads to poor interpretability and robustness. Focus on the context-dependent nature of anomaly detection, a robust unsupervised video anomaly detection framework based on knowledge and frame prediction is proposed, called VAD-KFP. Prior knowledge which contains the context of anomaly is introduced into the multi-path frame prediction network through multi-layer Graph Convolutional Networks. By integrating the prior knowledge to accurately define anomalies, VAD-KFP is robust to different scenarios and is able to recognize the type of anomaly. An extensive range of experiments have been conducted on three benchmarks, the results of which indicate that our method outperforms strong baselines. Specifically, VAD-KFP obtains an AUROC score of 91.6% for the Avenue dataset.