A Novel Method for Recognition, Localization, and Alarming to Prevent Swimmers from Drowning

Liu, Hanbing; Wen, Bo; Frej, Mohamed Ben Haj

doi:10.1109/cloudsummit47114.2019.00017

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…M. A. Hayat [3], [6] Yaswanth Kumar S K; Praveen O K, CAI Xiaoyang [10], L. Fei, W. Xueli [11], Soren Bonderup [12], [14] Ahmad Ilham, Sherly Prastica Della have proposed several methods by using image processing techniques to predict, detect and avoid drowning. A. Kulkarni [8] has proposed an embedded device housing several sensors to detect a drowning incident [4], [13].…”

Section: Literature Studymentioning

confidence: 99%

Robust Drown Alerting, Preventing and Autonomous Rescue System

Laxman¹,

Jain

2021

The Scientific Bulletin of Electrical Engineering Faculty

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Section: Literature Studymentioning

confidence: 99%

Robust Drown Alerting, Preventing and Autonomous Rescue System

Laxman¹,

Jain

2021

The Scientific Bulletin of Electrical Engineering Faculty

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“…In 2016, approximately 230,000 people died from drowning worldwide, which makes drowning the third leading cause of unintentional injury and death. Therefore, integrating IoT and ML technologies can play a huge role in automating the monitoring of swimming pool management systems, to reduce the occurrence of these incidents [7,8]. To fully automate a smart monitoring system without human involvement, computer vision techniques, which allow machines to see and understand the world through image capturing and processing, should be utilized and implemented.…”

Section: Introductionmentioning

confidence: 99%

Automated and Intelligent System for Monitoring Swimming Pool Safety Based on the IoT and Transfer Learning

Alotaibi

2020

Electronics

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Recently, integrating the Internet of Things (IoT) and computer vision has been utilized in swimming pool automated surveillance systems. Several studies have been proposed to overcome off-time surveillance drowning incidents based on using a sequence of videos to track human motion and position. This paper proposes an efficient and reliable detection system that utilizes a single image to detect and classify drowning objects, to prevent drowning incidents. The proposed system utilizes the IoT and transfer learning to provide an intelligent and automated solution for off-time monitoring swimming pool safety. In addition, a specialized transfer-learning-based model utilizing a model pretrained on “ImageNet”, which can extract the most useful and complex features of the captured image to differentiate between humans, animals, and other objects, has been proposed. The proposed system aims to reduce human intervention by processing and sending the classification results to the owner’s mobile device. The performance of the specialized model is evaluated by using a prototype experiment that achieves higher accuracy, sensitivity, and precision, as compared to other deep learning algorithms.

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