Publication

Abstract : The task of anomaly detection has recently gained much attention in the field of visual surveillance. Video surveillance data is often available in large quantities, but manual annotation of activities in video segments is tedious. Anomaly detection plays a crucial role in various indoor and outdoor surveillance applications. Video anomaly detection is highly challenging and provides a lot of scope and demand for improving detection performance in real-time scenarios. Recently, deep learning-based approaches are promising to detect single-scene video anomalies in real-time. This work starts by highlighting the over-view of deep learning-based video anomaly detection. A thematic taxonomy that includes four major categories and several sub-categories is presented. State-of-the-art deep learning approaches under these categories are reviewed. In addition, few recent one class model based deep learning approaches are evaluated and analyzed in terms of performance. Out of the approaches presented, Generative Adversarial Network (GAN) and Adversarial Autoencoder-based approaches provide a better detection rate. A few important directions are outlined for further research in the field of video surveillance applications.