Ohio State University (OSU) researchers monitored traffic using cameras already installed on the Campus Area Bus Service's transit buses.
They implemented a system that utilizes the YOLOv4 two-dimensional (2D) deep learning model to automatically identify and track objects; OSU's Keith Redmill said the model can recognize multiple objects in a single image frame.
The algorithm also can leverage streams of images, global navigation satellite system measurements, and regional data from 2D maps to project real-world overhead-view coordinates of the road network.
Said Redmill, "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."
From Ohio State News
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