A system for spatiotemporal anomaly localization in surveillance videos

Wu, H; Shao, J; Xu, X; Shen, F; Shen, HT

A system for spatiotemporal anomaly localization in surveillance videos

Wu, H Shao, J Xu, X Shen, F Shen, HT

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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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Field	Value	Language
dc.contributor.author	Wu, H	en_US
dc.contributor.author	Shao, J	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Shen, F	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-10-23	en_US
dc.identifier.citation	MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, 2017, pp. 1225 - 1226	en_US
dc.identifier.isbn	9781450349062	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126306
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	MM 2017 - Proceedings of the 2017 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/3123266.3127912	en_US
dc.title	A system for spatiotemporal anomaly localization in surveillance videos	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 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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http://hdl.handle.net/10453/126306