Unsupervised video anomaly detection via normalizing flows with implicit latent features

作者：

Highlights：

• Surveillance anomaly detection is critical in our daily life that replaces inefficient human monitoring with an automated system and provides various pattern recognition applications.

• In this paper, novel architecture ITAE learns normal appearance and motion patterns by implicitly capturing static and dynamic features.

• By utilizing normalizing flow generative model, we are the first to estimate the distribution of appearance and motion surveillance video features.

• The proposed approach achieves superior performance on six surveillance anomaly detection benchmarks and demonstrates its effectiveness of generalization ability which is crucial issue in real-world scenarios.

摘要

•Surveillance anomaly detection is critical in our daily life that replaces inefficient human monitoring with an automated system and provides various pattern recognition applications.•In this paper, novel architecture ITAE learns normal appearance and motion patterns by implicitly capturing static and dynamic features.•By utilizing normalizing flow generative model, we are the first to estimate the distribution of appearance and motion surveillance video features.•The proposed approach achieves superior performance on six surveillance anomaly detection benchmarks and demonstrates its effectiveness of generalization ability which is crucial issue in real-world scenarios.

论文关键词：Video anomaly detection,Surveillance system,AutoEncoder,Normalizing flow

论文评审过程：Received 5 November 2021, Revised 15 February 2022, Accepted 7 April 2022, Available online 11 April 2022, Version of Record 22 April 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108703