A Statistical Method for Counting Pedestrians in Crowded Environments

Abstract

We propose a statistical method for counting pedestrians. Previous pedestrian counting methods are not applicable to highly crowded areas because they rely on the detection and tracking of individuals. The performance of detection-and-tracking methods are easily degraded for highly crowded scene in terms of both accuracy and computation time. The proposed method employs feature-based regression in the spatiotemporal domain to count pedestrians. The proposed method is accurate and requires less computation time, even for large crowds, because it does not include the detection and tracking of objects. Our test results from four hours of video sequence obtained from a highly crowded shopping mall, reveal that the proposed method is able to measure human traffic with an accuracy of 97.2% and requires only 14 ms per frame.