Abstract:
Beaches serve as popular recreational spaces, attracting a multitude of visitors. However, the inherent risk of drowning poses a significant concern, necessitating advanced safety measures. This project tackles the critical issue of drowning incidents at beaches through the development of a sophisticated Drowning Detection System. The primary aim is to employ state-of-the-art computer vision techniques and object detection technology to actively monitor water activities, promptly identify potential drowning situations, and enhance overall beach safety.
The methodology employed in this project involves the integration of various technologies to
create a comprehensive solution. Surveillance cameras act as the primary data source, capturing live video feeds for analysis. Computer vision mechanisms are implemented to detect wave dynamics, providing crucial insights into the dynamics of the beach environment. Additionally, real-time object detection algorithms track human activities during swimming, contributing to the system's ability to identify anomalous behaviors indicative of potential drowning incidents. The tiered architecture ensures efficient system management, with separate layers dedicated to presentation, logic, and data.
Initial results demonstrate promising progress in the development of the prototype Drowning Detection System. Leveraging Python as the main programming language, the system showcases adaptability and effectiveness. Utilizing diverse datasets for model training ensures the system's robustness across varying beach conditions. The demonstration provides an overview of the system's functionalities and serves as a foundation for ongoing improvements. This innovative solution holds the potential to revolutionize beach safety, showcasing the transformative impact of emerging technologies in safeguarding lives.
