A system for spatiotemporal anomaly localization in surveillance videos

Publication Type:
Conference Proceeding
Citation:
MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, 2017, pp. 1225 - 1226
Issue Date:
2017-10-23
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© 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.
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