Drowning Detection System using LRCN Approach

• Published in: International journal for research in applied science and engineering technology 2022-04, Vol.10 (4), p.2980-2985
• Main Authors: Chavan, Shardul Sanjay, Dhake, Sanket Tukaram, Jadhav, Shubham Virendra, Mathew, rof. Johnson
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2022.41996

This project provides the insights of a real-time video surveillance system capable of automatically detecting drowning incidents in a swimming pool. Drowning is the 3rd reason for the highest unintentional deaths, and that’s why it is necessary to create trustable security mechanisms. Currently, most of the swimming pool's security mechanisms include CCTV surveillance and lifeguards to help in drowning situations. But this method is not enough for huge swimming pools like in amusement parks. Nowadays, some of the security systems are using AI for drowning detection using cameras situated underwater at a fixed location and also by using floating boards having a camera mounted on the bottom side so that underwater view can be captured. But the main problems in these systems arise when the pool is crowded and vision of cameras is blocked by people. In this project, rather than using underwater cameras, we are using cameras situated on top of the swimming pool to get an upper view of the swimming pool so that entire swimming pool will be under surveillance all time. Keywords: Computer vision, Convolutional neural network, Convlstm2D, LRCN, UCF50, OpenCV.