Elder, James HarveyPakdamansavoji, Sajjad2025-07-232025-07-232025-02-182025-07-23https://hdl.handle.net/10315/42975Classifying vehicle trajectories at intersections, known as turning movement counts (TMC), is a critical task for traffic management. Traditional approaches rely on a detect, track, count (DTC) paradigm that employs rule-based methods on image-plane data from a single camera. In this thesis, we propose a novel maximum likelihood approach that operates on the ground plane to perform trajectory classification. Our method demonstrates superior performance compared to image plane techniques and shows promising preliminary results for integrating multi-camera data on ground plane at the counting stage.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Artificial intelligenceComputer scienceTransportation planningElectronic Thesis or Dissertation2025-07-23Computer visionArtificial intelligenceTraffic monitoringObject detectionObject trackingTrajectory classification