This 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.
Papanikolopoulos, Nikolaos P.; Masoud, Osama; Richards, Charles A..
Pedestrian Control at Intersections - Phase I.
Minnesota Department of Transportation.
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