Passenger Counting in Public Rail Transport - Using Head-shoulder Contour Tracking

Automated people counting has multiple applications: referring passengers to vehicles with empty seats, gathering statistical information for railway companies to improve their distribution of vehicles, etc. In this paper, a people counting algorithm for public transport vehicles is presented. First, head-shoulder contours are detected by adaboost classification of a combination of a histogram of oriented gradients features and a color histogram. An integral histogram and integral image are used to speed up the extraction of these features. The results of the classification process are clustered and these clusters are tracked by a Kalman filter using a custom error covariance matrix. Finally, the path followed by an observed person is evaluated in order to count passengers entering and exiting the vehicle. Evaluation shows that this approach performs better than previous approaches, especially in scenarios with occlusions.