Water Level Detection from CCTV Cameras using a Deep Learning Approach
© 2020 IEEE. Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detec...
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th-mahidol.609062021-02-03T13:54:05Z Water Level Detection from CCTV Cameras using a Deep Learning Approach Punyanuch Borwarnginn Jason H. Haga Worapan Kusakunniran National Institute of Advanced Industrial Science and Technology Mahidol University Computer Science Engineering © 2020 IEEE. Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detection systems use remote sensors such as river water levels and rainfall to provide information to both disaster management professionals and the general public. There is an attempt to use visual information such as CCTV cameras to detect extreme flooding events; however, it requires human experts and consistent attention to monitor any changes. In this paper, we introduce an approach to the automatic river water level detection using deep learning to determine the water level from surveillance cameras. The model achieves 93% accuracy using a single camera location and 83% accuracy using multiple camera locations. 2021-02-03T06:21:58Z 2021-02-03T06:21:58Z 2020-11-16 Conference Paper IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.2020-November, (2020), 1283-1288 10.1109/TENCON50793.2020.9293865 21593450 21593442 2-s2.0-85098942424 https://repository.li.mahidol.ac.th/handle/123456789/60906 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098942424&origin=inward |
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Computer Science Engineering Punyanuch Borwarnginn Jason H. Haga Worapan Kusakunniran Water Level Detection from CCTV Cameras using a Deep Learning Approach |
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© 2020 IEEE. Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detection systems use remote sensors such as river water levels and rainfall to provide information to both disaster management professionals and the general public. There is an attempt to use visual information such as CCTV cameras to detect extreme flooding events; however, it requires human experts and consistent attention to monitor any changes. In this paper, we introduce an approach to the automatic river water level detection using deep learning to determine the water level from surveillance cameras. The model achieves 93% accuracy using a single camera location and 83% accuracy using multiple camera locations. |
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National Institute of Advanced Industrial Science and Technology |
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National Institute of Advanced Industrial Science and Technology Punyanuch Borwarnginn Jason H. Haga Worapan Kusakunniran |
format |
Conference or Workshop Item |
author |
Punyanuch Borwarnginn Jason H. Haga Worapan Kusakunniran |
author_sort |
Punyanuch Borwarnginn |
title |
Water Level Detection from CCTV Cameras using a Deep Learning Approach |
title_short |
Water Level Detection from CCTV Cameras using a Deep Learning Approach |
title_full |
Water Level Detection from CCTV Cameras using a Deep Learning Approach |
title_fullStr |
Water Level Detection from CCTV Cameras using a Deep Learning Approach |
title_full_unstemmed |
Water Level Detection from CCTV Cameras using a Deep Learning Approach |
title_sort |
water level detection from cctv cameras using a deep learning approach |
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2021 |
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https://repository.li.mahidol.ac.th/handle/123456789/60906 |
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1763493585066393600 |