A System for Spatiotemporal Anomaly Localization in Surveillance Videos
Huimin Wu, Jie Shao, Xing Xu, Fumin Shen, Heng Tao Shen · 2017
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/GAN in 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.