Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras

To formulate the schedules and routes of buses, bus companies monitor and gather data on the number of passengers and the boarding sections for each passenger several days a year. The problem is, however, that this monitoring is currently performed manually and requires a great deal of human cost. To solve this problem, recent proposals analyze the images taken by the surveillance cameras installed in most modern Japanese buses. The previous methods make it possible to identify the boarding sections regardless of the payment method like IC cards by matching people in the images obtained from different surveillance cameras. In this paper, we propose an improved method for estimating boarding sections; it uses minimum weight perfect matching on a bipartite graph; the assumption is that there exists one-to-one correspondence between people appearing in two surveillance camera images. In addition, the proposed method takes the boarding direction estimates output by person detection and tracking into account. To further improve the estimation accuracy, we employ a time constraint to handle the restricted movement of passengers on a bus. To confirm the effectiveness of the proposed method, we conduct experiments on the images taken by actual bus surveillance cameras. The results show that the proposed method achieves significantly better results than the previous method.