Activities in extended video

Ma, F; Li, P; Zhu, L; Dong, X; Liu, Y; Yang, Y

Activities in extended video

Ma, F Li, P

Zhu, L Dong, X

Liu, Y Yang, Y

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Publication Type:: Conference Proceeding
Citation:: 2018 TREC Video Retrieval Evaluation, TRECVID 2018, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Ma, F
dc.contributor.author	Li, P https://orcid.org/0000-0003-1809-2137
dc.contributor.author	Zhu, L
dc.contributor.author	Dong, X https://orcid.org/0000-0001-9272-1590
dc.contributor.author	Liu, Y
dc.contributor.author	Yang, Y
dc.date.accessioned	2021-05-14T01:10:34Z
dc.date.available	2021-05-14T01:10:34Z
dc.date.issued	2020-01-01
dc.identifier.citation	2018 TREC Video Retrieval Evaluation, TRECVID 2018, 2020
dc.identifier.uri	http://hdl.handle.net/10453/148893
dc.description.abstract	In this paper, we present a system based on detection, tracking and 3D convolution neural network dealing with Activities in Extended Video (ActEV) task in TRECVID 2018. In the proposed system, videos are first unfolded into frames for training detection network, then we use it to generate bounding box for tracking areas where target activities could be happen. The tracking clips are then classified using a 3D convulution network.
dc.language	en
dc.relation.ispartof	2018 TREC Video Retrieval Evaluation, TRECVID 2018
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Activities in extended video
dc.type	Conference Proceeding
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 Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-14T01:10:34Z
pubs.publication-status	Published

Abstract:

In this paper, we present a system based on detection, tracking and 3D convolution neural network dealing with Activities in Extended Video (ActEV) task in TRECVID 2018. In the proposed system, videos are first unfolded into frames for training detection network, then we use it to generate bounding box for tracking areas where target activities could be happen. The tracking clips are then classified using a 3D convulution network.

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http://hdl.handle.net/10453/148893