Papanikolopoulos, Nikolaos P.Masoud, OsamaRichards, Charles A.2013-08-122013-08-121996-10https://hdl.handle.net/11299/155340This report describes a real-time system for tracking pedestrians in sequences of grayscale images acquired by a stationary camera. The system outputs the spatio-temporal coordinates of each pedestrian during the period when the pedestrian is visible. Implemented on a Datacube MaxVideo 20 equipped with a Datacube Max 860, the system achieved a peak performance of over 30 framers per second. Experimental results based on indoor and outdoor scenes have shown that the system is robust under many difficult traffic situations. The system uses the "figure/ground" framework to accomplish the goal of pedestrian detection. The detection phase outputs the tracked blobs (regions), which in turn pass to the final level, the pedestrian level. The pedestrian level deals with pedestrian models and depends on the tracked blobs as the only source of input. By doing this, researchers avoid trying to infer information about pedestrians directly from raw images, a process that is highly sensitive to noise. The pedestrian level makes use of Kalman filtering to predict and estimate pedestrian attributes. The filtered attributes constitute the output of this level, which is the output of the system. This system was designed to be robust to high levels of noise and particularly to deal with difficult situations, such as partial or full occlusions of pedestrians. The report compares vision sensors with other types of possible sensors for the pedestrian control task and evaluates the use of active deformable models as an effective pedestrian tracking module.en-USCCD camerasPedestrian controlComputer vision techniquesVisual trackingVisual detectionReport