Traffic Surveillance

Traffic surveillance research focuses on developing automated systems for monitoring and analyzing traffic conditions using video feeds from cameras, aiming to improve safety, efficiency, and management of transportation networks. Current research emphasizes real-time processing using deep learning architectures like YOLO and other object detection models, coupled with advanced techniques such as object tracking, trajectory analysis, and low-light image enhancement. These advancements enable applications ranging from detecting traffic violations and accidents to predicting air pollution levels based on vehicle density, ultimately contributing to safer and more efficient transportation systems.