Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Automatically creating spatio-temporal occupancy analysis of public swimming pools is of great interest, both for administrators to optimize the use of these expensive facilities, and for users to schedule their activities outside peak hours. In this paper we apply current state-of-the-art deep learning methods within human detection on low quality swimming pool video. Furthermore, we propose a method for analyzing the spatio-temporal occupancy of a swimming pool. We show that it is possible to precisely detect swimmers in very challenging conditions by obtaining an AUC of 93.48 % from YOLOv2. An acceptable AUC of 79.29 % was obtained from Tiny-YOLO, which can be implemented on a low-cost embedded system capable of producing results in real-time on site. We expect that the performance of both networks can be improved with more training data.
OriginalsprogEngelsk
TitelACM Multimedia Conference Workshops : First International Workshop on Multimedia Content Analysis in Sports
ForlagAssociation for Computing Machinery
Publikationsdatookt. 2018
Sider67-73
ISBN (Trykt)978-1-4503-5981-8
DOI
StatusUdgivet - okt. 2018
BegivenhedACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports - Lotte Hotel Seoul, Seoul, Sydkorea
Varighed: 22 okt. 201826 okt. 2018

Konference

KonferenceACM Multimedia Conference Workshops
LokationLotte Hotel Seoul
LandSydkorea
BySeoul
Periode22/10/201826/10/2018

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Swimming pools
Embedded systems
Deep learning
Costs

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Jensen, M. B., Gade, R., & Moeslund, T. B. (2018). Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video. I ACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports (s. 67-73). Association for Computing Machinery. https://doi.org/10.1145/3265845.3265846
Jensen, Morten Bornø ; Gade, Rikke ; Moeslund, Thomas B. / Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video. ACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, 2018. s. 67-73
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title = "Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video",
abstract = "Automatically creating spatio-temporal occupancy analysis of public swimming pools is of great interest, both for administrators to optimize the use of these expensive facilities, and for users to schedule their activities outside peak hours. In this paper we apply current state-of-the-art deep learning methods within human detection on low quality swimming pool video. Furthermore, we propose a method for analyzing the spatio-temporal occupancy of a swimming pool. We show that it is possible to precisely detect swimmers in very challenging conditions by obtaining an AUC of 93.48 {\%} from YOLOv2. An acceptable AUC of 79.29 {\%} was obtained from Tiny-YOLO, which can be implemented on a low-cost embedded system capable of producing results in real-time on site. We expect that the performance of both networks can be improved with more training data.",
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Jensen, MB, Gade, R & Moeslund, TB 2018, Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video. i ACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, s. 67-73, ACM Multimedia Conference Workshops, Seoul, Sydkorea, 22/10/2018. https://doi.org/10.1145/3265845.3265846

Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video. / Jensen, Morten Bornø; Gade, Rikke; Moeslund, Thomas B.

ACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, 2018. s. 67-73.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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Jensen MB, Gade R, Moeslund TB. Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video. I ACM Multimedia Conference Workshops: First International Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery. 2018. s. 67-73 https://doi.org/10.1145/3265845.3265846