Mobile Surveillance System using Unmanned Aerial Vehicle for Aerial Imagery

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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 "images in the wild." CSRNet’s ability to leverage dilated convolutions allows it to effectively capture contextual information and handle high crowd densities without sacrificing spatial resolution. We evaluate the model’s performance on diverse datasets, including aerial imagery and real-world riot scenarios, focusing on its adaptability to dynamic, unstructured environments. The results demonstrate the potential of CSRNet to provide accurate crowd density estimates under adverse conditions, offering critical insights for public safety applications. By addressing the technical challenges of implementing CSRNet in these contexts, this study contributes to the advancement of deep learning-based crowd counting, emphasizing its significance in real-world scenarios such as riots and other high-stakes events. Future work aims to further enhance the model's robustness and applicability to diverse operational settings.