Automated vehicle counting technology has been in use for many years, but developments in automated pedestrian counting technology have been limited. Pedestrians are more difficult to detect, track and count because their paths are much less constrained. In this paper, we present an advanced pedestrian counting system using a stereo camera and a laser scanner. A mapping algorithm has been developed to map the detection locations in the laser scanner coordinates to the stereo-image coordinates. For pedestrian tracking, we apply the nonparametric statistical hypothesis tests such as KolmogorovSmirnov test for association of close tracks, and incorporate pedestrian image features such as SIFT (Scale Invariance Feature Transform) into Kalman filter for multi-pedestrian tracking. Test results based on the data collected at a street intersection have demonstrated that this pedestrian counting system can accurately detect, track and count multiple pedestrians walking in a large group.
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