Edge-AI-Enabled Passenger Tracking for Public Buses with Face Recognition and GPS

Abstract

In many developing countries, the counting of passengers in buses is done manually, resulting in a loss of revenue for the transportation companies. This paper proposes an IoT-based tracking system for passengers in buses using face recognition technology to count the passengers accurately and compute the fares using the GPS device. The proposed system employs ESP32-CAM devices with lightweight neural networks to recognize faces and embed the faces at the entry and exit points of the bus. A two-point matching technique using the cosine similarity function is used to match the faces at the entry and exit points of the bus to identify the passengers. At the same time, the GPS device is used to analyze the waypoints to compute the route taken by the bus, thereby calculating the fares accurately. The data is stored in CSV format to operate the device offline and then synchronized with the cloud server for session management. The proposed system achieved face recognition accuracy of more than 95% and route detection accuracy of more than 90%. The proposed system is feasible for edge-AI-based IoT devices for intelligent transportation systems, as the device is reliable and costs only USD 15.