Leveraging Unsupervised and Self-Supervised Learning for Video Anomaly Detection

Video anomaly detection consists of detecting abnormal events in videos.Since abnormal events are rare, anomaly detection methods are mainly not fully supervised.One such popular family of methods learn normality by training an autoencoder (AE) on normal data and detect anomalies as they deviate from this normality.But the powerful reconstruction capacity of AE makes it still difficult to separate anomalies from normality.To address this issue, some works enhance the AE with an external memory bank or attention modules but still these methods suffer in detecting diverse spatial and temporal anomalies.In this work, we propose a method that leverages unsupervised and self-supervised learning on a single AE.The AE is trained in an end-to-end manner and jointly learns to discriminate anomalies using three chosen tasks: (i) unsupervised video clip reconstruction; (ii) unsupervised future frame prediction; (iii) self-supervised playback rate prediction.Furthermore, to correctly emphasize the detected anomalous regions in the video, we introduce a new error measure, called the blur pooled error.Our experiments reveal that the chosen tasks enrich the representational capability of the autoencoder to detect anomalous events in videos.Results demonstrate our approach outperforms the state-of-the-art methods on three public video anomaly datasets.