Researchers have proposed a novel method for counting and tracking vehicles on public roads, a development that could enhance current traffic systems and help travelers get to their destinations faster. Using the cameras already installed on campus buses at The Ohio State University, researchers demonstrated that they could automatically and accurately measure counts of vehicles on urban roadways, could detect objects in the road, and could distinguish parked vehicles from those that are moving.
In previous studies, Ohio State researchers found that using these mobile cameras provides much better spatial and temporal coverage than relying on sparsely and often temporarily placed sensors that don’t provide a view of many streets and roads in a city. “If we collect and process more comprehensive high-resolution spatial information about what’s happening on the roads, then planners could better understand changes in demand, effectively improving efficiency in the broader transportation system,” said Keith Redmill, lead author of the study and a research associate professor of electrical and computer engineering at Ohio State. Whereas researchers previously used human observers to manually identify the vehicles in the videos, this study, published in the journal Sensors, automates the process using AI. The system works by utilizing a state-of-the-art 2D deep learning model called YOLOv4 to automatically detect and track objects. The program is also uniquely adept at recognizing multiple objects in a single image frame, said Redmill.
Overview of the proposed automated vehicle counting system.
