Argus: Efficient Activity Detection System for Extended Video Analysis

Liu, W; Kang, G; Huang, PY; Chang, X; Yu, L; Qian, Y; Liang, J; Gui, L; Wen, J; Chen, P; Hauptmann, AG

Argus: Efficient Activity Detection System for Extended Video Analysis

Liu, W Kang, G Huang, PY Chang, X

Yu, L Qian, Y Liang, J Gui, L Wen, J Chen, P Hauptmann, AG

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020, 2020, 00, pp. 126-133
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Kang, G
dc.contributor.author	Huang, PY
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Yu, L
dc.contributor.author	Qian, Y
dc.contributor.author	Liang, J
dc.contributor.author	Gui, L
dc.contributor.author	Wen, J
dc.contributor.author	Chen, P
dc.contributor.author	Hauptmann, AG
dc.date	2020-03-01
dc.date.accessioned	2023-03-31T10:19:41Z
dc.date.available	2023-03-31T10:19:41Z
dc.date.issued	2020-03-01
dc.identifier.citation	Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020, 2020, 00, pp. 126-133
dc.identifier.isbn	9781728171623
dc.identifier.issn	2572-4398
dc.identifier.uri	http://hdl.handle.net/10453/168980
dc.description.abstract	We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, we extract several different features and apply sequential activity classification with them. Finally, we eliminate inaccurate events and fuse all the predictions from different features. The proposed system wins Trecvid Activities in Extended Video (ActEV1) challenge 2019. It achieves the first place with 60.5 mean weighted Pmiss, outperforming the second place system by 14.5 and the baseline R-C3D by 29.0. In TRECVID 2019 Challenge2, the proposed system wins the first place with pAUDC@0.2tfa 0.48407.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020
dc.relation.ispartof	IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
dc.relation.ispartofseries	IEEE Winter Conference on Applications of Computer Vision Workshops
dc.relation.isbasedon	10.1109/WACVW50321.2020.9096929
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Argus: Efficient Activity Detection System for Extended Video Analysis
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Snowmass, CO
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	in_progress	*
dc.date.updated	2023-03-31T10:19:39Z
pubs.finish-date	2020-03-05
pubs.publication-status	Published
pubs.start-date	2020-03-01
pubs.volume	00

Abstract:

We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, we extract several different features and apply sequential activity classification with them. Finally, we eliminate inaccurate events and fuse all the predictions from different features. The proposed system wins Trecvid Activities in Extended Video (ActEV1) challenge 2019. It achieves the first place with 60.5 mean weighted Pmiss, outperforming the second place system by 14.5 and the baseline R-C3D by 29.0. In TRECVID 2019 Challenge2, the proposed system wins the first place with pAUDC@0.2tfa 0.48407.

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http://hdl.handle.net/10453/168980