Video surveillance system for realtime flood detection and mobile app for flood alert

Menon, K. Priya; Kala, L.

doi:10.1109/iccmc.2017.8282518

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…In a live camera approach, Bhola et al [16] used the size of bridges and the detected water surface in the images to estimate the water level. Further, Menon and Kala [39] used a region-based image segmentation method (GrowCut) to detect the flood extent and provide warning information by mobile app.…”

Section: B Sensing From In Situ Video Streamingmentioning

confidence: 99%

Deep Sensing of Urban Waterlogging

Chang

et al. 2021

IEEE Access

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In the monsoon season, sudden flood events occur frequently in urban areas, which hamper the social and economic activities and may threaten the infrastructure and lives. The use of an efficient largescale waterlogging sensing and information system can provide valuable near real-time disaster information to facilitate disaster management and enhance awareness of the general public to alleviate losses during and after flood disasters. Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging sensing and event-location mapping. The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale. The system could sense approximately 2379 vision sources through an internet of video things framework and transmit the event-location information in 5 min. The proposed approach can sense waterlogging events at a national scale and provide an efficient and highly scalable alternative to conventional waterlogging sensing methods.INDEX TERMS deep neural network, internet of video things, urban flood, urban waterlogging, visual sensing

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Section: B Sensing From In Situ Video Streamingmentioning

confidence: 99%

Deep Sensing of Urban Waterlogging

Chang

et al. 2021

IEEE Access

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“…The first test tested the total amount of time; the second test determined whether the machine should use three LEDs as its early warning mechanism to alert people from afar. In 2015, Menon et al [15] proposed that the surface water transformation identity is a combination of image features. Subsequently, to retrieve and map the described modifications, an ANN support vector machine (SVM) and maximum probability (ML) classification techniques were used.…”

Section: Introductionmentioning

confidence: 99%

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

Dhaya¹,

Kanthavel²

2022

Intelligent Automation &Amp; Soft Computing

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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 classifiers using a Convolutional Neural Network (CNN), which is utilized to classify the water level in the images. The performance analysis of the method is analyzed by various parameters. The accuracy of the proposed method is 11% higher than that of the k-Nearest Neighbors (KNN) classifiers and 5% higher than that of the ANN classifiers, and the processing time is 7% less than that of the KNN classifiers and 4% less than that of the Artificial Neural Network (ANN) classifiers.

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“…It should enable them take the necessary steps to cope with the tragedy that has happened. The same study has been used by [11] - [13] that uses video as flood monitoring and by [14] - [18] using the power of machine vision deep learning.…”

Section: Introductionmentioning

confidence: 99%

An Inference Approach of Flood Level Detection and Alert System: Flood-Induced Simulated Environment

Alon¹,

Mindoro²,

Casuat³

et al. 2020

IJETER

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Many kinds of research focused on the flood detection and monitoring, flood management, flood risk management and flood forecasting in urban areas, wherein a large number of populations lies chaos in mobility is high. Owing to natural disasters, flooding in these regions can lead to an increase in mortality rates. This project is primarily focused on the detection of a flood by installing a flood detector device with a camera beside the bridge column. The camera is facing the three lines with different colors. If one of the colors was tempered by the river water, the device will send an alarm to the community that the water level in the river is high. This aims to alert the community and the authorities to be aware and be ready for the approaching flood. Flood-Level Detection and Alert System proved 87.1%, 73.6%, and 95.69% testing accuracy of Green, Blue, and Red respectively. Overall, the accuracy of the whole system produced 85.46%.

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Video surveillance system for realtime flood detection and mobile app for flood alert

Cited by 11 publications

References 9 publications

Deep Sensing of Urban Waterlogging

Deep Sensing of Urban Waterlogging

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

An Inference Approach of Flood Level Detection and Alert System: Flood-Induced Simulated Environment

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