Video-based automatic transit vehicle ingress/egress counting using trajectory clustering

Abstract

In this paper we present an automatic vehicle ingress/egress counting method by clustering dense trajectories extracted from monitoring videos. Dense trajectories are extracted based on dense optical flow when passengers cross the door of the vehicle, and then clustered into different groups according to their descriptors with each legitimate group as a passenger. The contribution of the proposed method is twofold. First, we put forward an online passenger counting framework which is based on feature-points tracking and can be easily deployed to different scenarios. The method works even in low illumination conditions as demonstrated in experiments. Second, vehicle running information was combined to improve the accuracy of passenger counting. The transit vehicle settings are unconstrained and complex due to variations from illumination, movement and uncontrolled passenger behaviors. We tackle this by incorporating different modalities besides videos such as the status of the vehicle (e.g., in motion or not). The experimental results on multiple real bus videos show that the proposed system can count passengers with average accuracy of 94.9% at an average frame rate of 38 fps.