Traffic Monitoring

Traffic monitoring research aims to improve the efficiency and safety of transportation systems through automated analysis of traffic data. Current efforts focus on developing advanced algorithms, including deep learning models like convolutional and recurrent neural networks, to process diverse data sources such as video from various perspectives (street cameras, satellites, drones), acoustic recordings, and even event-based cameras. These advancements enable more accurate predictions of traffic flow, improved vehicle detection and tracking (including multi-object tracking and 3D tracking), and enhanced classification of vehicle types, all contributing to better traffic management and potentially reducing accidents. The development of large, publicly available datasets is also a key focus, facilitating the comparison and improvement of different algorithms.