Conventional traffic monitoring methods are plagued with challenges related to efficiency, accuracy, and accessibility, prompting a surge in research aimed at developing automated traffic monitoring systems. Among these procedures, studies have increasingly focused on leveraging Unmanned Aerial Vehicles (UAVs) as data collection platforms with cutting-edge computer vision algorithms. The agility and versatility of UAVs as a mobile platform for data collection, along with the ability of computer vision algorithms, both conventional and AI-based, enables highly efficient and automated traffic analysis.
This talk will delve into the potential of UAVs as a highly effective platform for traffic data collection and the power of computer vision algorithms to automate the counting of vehicles in such data. Our seminar will provide a comprehensive overview of our research efforts to perform AI-based vehicle counting using real-world field data, covering key features, results, and valuable lessons learned. Furthermore, we aim to showcase the transformative potential of UAVs and computer vision algorithms in revolutionizing traffic monitoring, significantly enhancing efficiency and accuracy in traffic management.
Mr. Tewodros Gebre is a Ph.D. student at North Carolina A&T State University. He holds a MSc in Transport Engineering and a BSc degree in Hydraulic and Water Resource Engineering. As a student researcher at the NC-CAV Center research, Mr. Gebre has worked on developing innovative solutions for automating traffic data analysis. His research focuses on utilizing cutting-edge computer vision algorithms and Unmanned Aerial Vehicles (UAVs) as traffic data collection platforms. Mr. Gebre's image processing research contributes to latest studies towards developing highly efficient automated systems that can rapidly analyze data and deliver valuable insights is essential for critical decision-making processes.