Remote Sensing based Traffic Monitoring via Semantic Segmentation and Deep Learning

The rapid evolution of traffic monitoring demands innovative solutions capable of handling the complexity and scale of modern urban environments. Unlike traditional systems dependent on fixed roadside cameras, our approach leverages aerial platforms' flexibility and expansive coverage to offer a more dynamic and scalable solution. The framework begins with a novel preprocessing pipeline utilizing Gaussian reduction to enhance image quality and preserve critical vehicle features. Vehicle detection is powered by RetinaNet, a state-of-the-art detector that excels in challenging real-world conditions. Semantic segmentation with DeepLabV3+ further vehicle delineation, ensuring precise boundary identification. We implement an optical flow-based matching algorithm address the challenges of occlusions and consistency, enabling seamless tracking. For counting, R-CNN-based framework delivers highly accurate volume estimates with remarkable scalability. vehicle tracking is achieved using a particle filter, maintaining robust continuous trajectories. Rigorous validation on the Aerial Car Dataset demonstrates outstanding performance, with detection accuracy of 92% and tracking accuracy of 89%. This framework not only outperforms existing methods in precision and scalability but also sets a new benchmark intelligent traffic systems, smart cities, and aerial surveillance, pushing the boundaries of what is achievable in automated traffic monitoring.