Evaluation of Recursive Background Subtraction Algorithms for Real-Time Passenger Counting at Bus Rapid Transit System

Abstract

This work discusses a method to count the number of passengers waiting in Bus Rapid Transit station. The proposed system relies on computer vision technique to monitor the movement of passengers crossing doors on the station. In this work, three background subtraction techniques, namely, Running Gaussian Average, Gaussian Mixture Model, and Adaptive Gaussian Mixture Model, were used to count the passengers crossing an entrance on a BRT station from a pre-recorded motion picture. The results indicates that the tree algorithms are able to identify the passenger crossing with a reasonable high level of recall and but low level of precision. These results indicates that many false positives are identified by the three algorithms. In addition, the empirical data indicate that the three algorithms tend to have better performance with higher value of the learning rate.