DETECTING ANOMALIES IN VIDEOS USING MEMORY-AUGMENTED AUTOENCODER WITH KEY FRAME SELECTION

Abstract

In this article, we propose a novel method to train a memory-augmented autoencoder in supervised mode by generating pseudo abnormal videos based on key frame selection techniques. Most video anomaly detection methods employ a machine learning model to learn patterns of normal videos. Any video where the patterns significantly deviate from the learnt patterns is considered an anomaly. However, developing an effective machine learning model for video anomaly detection is a challenging task due to the deficiency of anomalies. Specifically, abnormal samples are often much rarer and harder to collect than normal samples. To address this problem, we propose a novel approach using key frame selection techniques to generate pseudo anomalies. The generated pseudo anomalies are then combined with normal data to create the augmented dataset. The memory-augmented autoencoder is then trained on the augmented datasets. The experimental results show that the AUC scores of the proposed solution are higher than those of the base network architecture from 0.20% to 1.31% on three well-known datasets for video anomaly detection.