Enhancing Object Detection with FOMO: A User-Friendly Smart Application for Real-Time Tracking of Modern Bus and Seat Availability

Abstract

This paper introduces a Smart Assistance for Public Transport System to address challenges like bus arrival time prediction and seat vacancy. Leveraging GPS technology and ESP32 CAM microcontrollers integrated with the Faster Objects, More Objects (FOMO) machine learning technique, this system offers real-time vehicle tracking and object detection capabilities. This research aims to design and implement a smart assistance system for public transport, focusing on modern buses, to enhance the efficiency of public transportation systems. It encompasses the development of a GPS-based plug-and-play device, a user-friendly smart application, and an evaluation of the proposed system through implementation and testing. The methodology involves system design for real-time tracking, passenger counting, and seating availability, along with implementing GPS systems and developing a smart application. Node.js, React Native, and Expo are utilized for backend and frontend development, ensuring seamless integration and functionality.