Development of an entry-exit decision algorithm for bus passenger counting using cameras

In recent years, entry-exit management technologies have gained attention due to the rise of smart cities and the proliferation of IoT devices. These technologies are used not only in indoor environments like offices and retail stores but also in outdoor settings such as public transportation. Particularly in public transport, there is growing demand for Origin Destination (OD) data collection to better understand passenger movement. In this study, we developed a system to estimate the number of passengers boarding and alighting from buses by installing cameras at the bus doors and capturing video footage of passengers. The system utilizes person detection and tracking algorithms to estimate passenger trajectories and applies a deep neural network (DNN) model to classify whether a detected individual is boarding or alighting. We introduced a DNN model for binary classification to determine whether individuals are inside or outside the bus, using bounding box and time information. By performing a grid search, we identified optimal model parameters. The system was tested using both YOLOR and the latest YOLOv10 object detectors, and the MPNTrack person tracking algorithm. The combination of YOLOv10 and the DNN model achieved an error rate of 11.7% for the entrance camera, while YOLOR and the DNN model achieved an error rate of 0.803% for the exit camera, significantly improving the accuracy over previous methods. This study demonstrates that integrating object detection, tracking, and DNN-based classification improves the accuracy of passenger counting systems, which is crucial for the effective management of public transportation systems.