Deep Learning-based Modular Framework for Risk Mitigation through Crowd Counting

Crowd counting, a crucial task in visual surveillance and automated public monitoring, entails accurately determining the number of individuals within a given image. While significant progress has been made in this domain, existing solutions often exhibit limitations, such as requirements for individuals to be in motion, simplistic backgrounds, or high image resolutions. However, real-world scenarios typically involve both moving and stationary individuals, complex backgrounds, and videos with relatively low resolutions in visual surveillance systems. Consequently, achieving accurate crowd counting and human identification in such contexts remains a significant challenge. The primary objective is to develop a robust vision system capable of monitoring activities within a site over extended durations. To address these challenges, we propose an innovative crowd counting application leveraging artificial neural network methodologies, particularly utilizing the latest iteration of the YOLO (You Only Look Once) framework. This approach operates on live video streams in real-time, offering instantaneous analytics that can substantially enhance security measures and inform infrastructure development initiatives. By harnessing the power of deep learning and real-time analytics, our proposed framework aims to overcome the limitations of existing crowd counting methods and provide actionable insights for risk management applications.