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    Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network

    R. Dhaya1,*, R. Kanthavel2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.021538

    Abstract The high prevalence of urban flooding in the world is increasing rapidly with the rise in extreme weather events. Consequently, this research uses an Automatic Flood Monitoring System (ARMS) through a video surveillance camera. Initially, videos are collected from a surveillance camera and converted into video frames. After converting the video frames, the water level can be identified by using a Histogram of oriented Gradient (HoG), which is used to remove the functionality. Completing the extracted features, the frames are enhanced by using a median filter to remove the unwanted noise from the image. The next step is water level… More >

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