Passenger flow counting in buses based on deep learning using surveillance video

An efficient traffic management system is crucial for public transportation. If the passenger flow can be detected accurately and instantaneously, the routes and schedules for public transportation can be effectively improved. However, previous research identified many challenges in passenger counting, such as messy image background, variations in lighting, and occlusions. In this paper, we propose a passenger flow counting model for buses, based on deep learning. First, we design a straightforward way to understand the opening state of the door. Next, a single shot multibox detector is used to learn the features of passengers and detect them. Finally, a particle filter with a three-step cascaded data association scheme is used for passenger tracking. To demonstrate the performance of the proposed algorithm, surveillance videos of three different situations, i.e., day, night, and a rainy day, are selected. Additionally, to make the system applicable to real cases, a few special scenes such as different objects worn by the passengers, passenger occlusions, and dense crowds, are considered. According to the experimental results, our method exhibits better performance than some existing methods.