A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well

A system for spatiotemporal anomaly localization in surveillance videos

© 2017 Copyright held by the owner/author(s). Anomaly detection and localization in surveillance videos have attracted broad attention in both academic and industry for its importance to public safety, which however remain challenging. In this demonstration, we propose an anomaly detection algorithm called 2stream-VAE/GAN by embedding VAE/GANin a two-stream architecture. By taking both spatial and temporal information into consideration, normality can be captured and anomaly detection can be achieved. With an outlier detection rule, the system automatically locates anomaly based on a pre-trained model, which suits well for both streaming and local videos