Automatic vehicle classification using wireless magnetic sensor

Abstract

This paper proposes an extension to our previous work on an automatic low-computed vehicle classification using embedded wireless magnetic sensor. A realization of our vehicle classification on embedded wireless magnetic sensor is studied in this work. The implementation allows real-time vehicle classification based on vehicle magnetic length, averaged energy, and Hill-pattern peaks. The system automatically detects vehicles, extracts features, and classifies them. The three features are of low-computation. We classify vehicles into 4 types: motorcycle, car, pickup and van. The classification shows a promising result. It can classify motorcycle with 95% accuracy. The classification rates between 70%-80% are achieved with car, pickup and van due to their similarity in these extracted features. The results obtained are comparable to our implementation using PC in our previous work and demonstrate that the algorithm can be realized on the embedded wireless magnetic sensor.