Automatic Sheep Behaviour Analysis Using Mask R-CNN

Xu, J; Wu, Q; Zhang, J; Tait, A

Automatic Sheep Behaviour Analysis Using Mask R-CNN

Xu, J

Wu, Q

Zhang, J

Tait, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 Digital Image Computing: Techniques and Applications (DICTA), 2021, 00, pp. 01-06
Issue Date:: 2021-12-23

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	AUTOMATIC_SHEEP_BEHAVIOUR_ANALYSIS_USING_MASK_R-CNN.pdf	Published version	6.23 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Xu, J https://orcid.org/0000-0002-9102-3616
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Tait, A
dc.date	2021-11-29
dc.date.accessioned	2022-06-07T01:19:42Z
dc.date.available	2022-06-07T01:19:42Z
dc.date.issued	2021-12-23
dc.identifier.citation	2021 Digital Image Computing: Techniques and Applications (DICTA), 2021, 00, pp. 01-06
dc.identifier.isbn	978-1-6654-1709-9
dc.identifier.uri	http://hdl.handle.net/10453/157985
dc.description.abstract	The issue of sheep welfare during live exports has triggered a lot of public concern recently. Extensive research is being carried out to monitor and improve animal welfare. Stocking density can be a critical factor affecting sheep welfare during export and its impact can be monitored through sheep behaviour, position, group dynamics and physiology. In this paper we demonstrate the application of the instance segmentation method Mask R-CNN to support sheep behaviour recognition. As an initial step, two typical behaviours standing and lying are recognized under different group sizes in pens over time. 94%+ mAP was achieved in the validation set demonstrating the effectiveness of the method on identifying sheep behaviours. Further data analysis will provide available space requirements for additional sheep allocation and daily behaviour monitoring to detect abnormal cases which will aim to improve the health and wellbeing of sheep on ships.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartof	Digital Image Computing: Techniques and Applications
dc.relation.isbasedon	10.1109/dicta52665.2021.9647101
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Automatic Sheep Behaviour Analysis Using Mask R-CNN
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Gold Coast, Australia
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 - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-07T01:19:39Z
pubs.finish-date	2021-12-01
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-11-29
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The issue of sheep welfare during live exports has triggered a lot of public concern recently. Extensive research is being carried out to monitor and improve animal welfare. Stocking density can be a critical factor affecting sheep welfare during export and its impact can be monitored through sheep behaviour, position, group dynamics and physiology. In this paper we demonstrate the application of the instance segmentation method Mask R-CNN to support sheep behaviour recognition. As an initial step, two typical behaviours standing and lying are recognized under different group sizes in pens over time. 94%+ mAP was achieved in the validation set demonstrating the effectiveness of the method on identifying sheep behaviours. Further data analysis will provide available space requirements for additional sheep allocation and daily behaviour monitoring to detect abnormal cases which will aim to improve the health and wellbeing of sheep on ships.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157985