A Novel Approach Towards Development of Automated Intelligent Lifesaving Buoy

Abstract

The main goal of this integrated application is to design an intelligently controlled and automated lifebuoy that can detect living people and save them from drowning. The article gives an overview of the development of a battery-powered remote-control boat for various applications. The Lifebuoy is a revolutionary upgrade of an invention created 300 years ago, including a remote control, thrusters, and a battery that can be sent to someone in the water in need. It's a clever lifesaver that gives these lifebuoys a U-shape to give a person who is drowning or injured and unable to swim a good grip to keep and stay in the U-shaped area of the buoy remain. The U-shaped lifebuoy can be moved through the water by remote control, allowing it to reach the victim quickly and bring them to safety if necessary. The U-shaped remote-controlled buoy can work in adverse conditions thanks to its navigation and guidance systems. The remote-controlled submarine buoy is a quick and effective way to save lives. Once it reaches the victim, the U-shaped buoy has enough power to carry it to safety, which can be very useful on large ships that would otherwise have to launch lifeboats. It also allows those on board to start the device without having to turn around, saving time at the beginning of a rescue operation. The waves can't stand up to the U-shaped buoy that you'll see racing by to reach your destination. Water is a dangerous place, and despite the fact that a remote-controlled U-shaped buoy is a lifesaver, people are easy to reach. Object tracking is an important task in many image-processing applications. Optical flow is one of the most widely used image processing and video analysis techniques. This article implements an object-tracking algorithm based on the optical flow method for computation on a Raspberry Pi microcomputer. The Lucas-Kanade method was used to compute the velocity vector of an object moving between two consecutive frames. This paper represents a face recognition mechanism carried out as part of the developing an intelligent lifebuoy. It utilises the technologies available in the Open Computer Vision (OpenCV) library and the methodology for their implementation with Python. Haar Cascades was used for face detection, and eigenfaces, fisherfaces, and local binary pattern histograms were used for face recognition. An experiment was performed to evaluate the robustness of the proposed algorithm against the new computing device. The results were encouraging for the use of the proposed real-time application in a variety of contexts.