2D Pose Estimation based Child Action Recognition

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DC Field	Value	Language
dc.contributor.author	Mohottala, S	-
dc.contributor.author	Abeygunawardana, S	-
dc.contributor.author	Samarasinghe, P	-
dc.contributor.author	Kasthurirathna, D	-
dc.contributor.author	Abhayaratne, C	-
dc.date.accessioned	2023-01-23T10:47:28Z	-
dc.date.available	2023-01-23T10:47:28Z	-
dc.date.issued	2022-11	-
dc.identifier.citation	S. Mohottala, S. Abeygunawardana, P. Samarasinghe, D. Kasthurirathna and C. Abhayaratne, "2D Pose Estimation based Child Action Recognition," TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), Hong Kong, Hong Kong, 2022, pp. 1-7, doi: 10.1109/TENCON55691.2022.9977799.	en_US
dc.identifier.issn	21593442	-
dc.identifier.uri	https://rda.sliit.lk/handle/123456789/3141	-
dc.description.abstract	We present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with LRCN on a benchmark dataset containing unconstrained environment based videos.	en_US
dc.language.iso	en	en_US
dc.publisher	Institute of Electrical and Electronics Engineers Inc.	en_US
dc.relation.ispartofseries	IEEE Region 10 Annual International Conference, Proceedings/TENCON;	-
dc.subject	child action recognition	en_US
dc.subject	graph convolutional networks	en_US
dc.subject	Long-term recurrent convolutional network	en_US
dc.subject	transfer learning	en_US
dc.title	2D Pose Estimation based Child Action Recognition	en_US
dc.type	Article	en_US
dc.identifier.doi	10.1109/TENCON55691.2022.9977799	en_US
Appears in Collections:	Department of Information Technology

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