An IoT-based Framework for Low-Cost and Light-Weight Vehicle Detection
Chandra Shekhar, Sudipta Saha · 2022
Real-time monitoring of traffic is a very significant issue in the context of a smart-city/intelligent-transportation system. It plays important role in traffic analysis as well as deriving plans for traffic-management, e.g., carrying out dynamic traffic re-routing, load-distribution etc. Detection and classification of the types of the vehicles passing over the roads are the prime components of traffic monitoring. The solutions proposed so far to accomplish the task mostly require installation of heavy and costly infrastructure (e.g., video camera, costly setup etc). They also incur high maintenance cost, as well as require constant power-supply to function. In this work, we propose an IoT-assisted low-power, low-cost, flexible and easy-to-install solution for detection and classification of vehicles in real-time. The proposed solution is also capable carrying out the job in a time-correlated manner over a wide-area. Through extensive outdoor measurement studies we demonstrate its high precision detection capability.