Mobile Surveillance System using Unmanned Aerial Vehicle for Aerial Imagery - Haq, Muhamad Amirul (Universitas Muhammadiyah Yogyakarta, 2024)
Crowd counting plays a vital role in public safety, particularly during riot scenarios where understanding crowd dynamics is crucial for effective decision-making and risk mitigation. Accurate crowd estimation in such environments enables authorities to monitor the situation in real time, allocate resources efficiently, and prevent potential escalations. However, counting individuals in a riot scenario presents unique challenges due to the chaotic nature of the scene, varying crowd densities, and obstructions caused by movement and environmental factors. Traditional methods struggle to provide reliable results in these conditions, necessitating advanced solutions. This study explores the implementation of CSRNet (Congested Scene Recognition Network), a state-of-the-art deep learning model, to address crowd counting in challenging environments characterized as