Vehicle Classification Based on Feature Selection With Anisotropic Magnetoresistive Sensor

Abstract

Vehicle classification based on magnetic sensors can be effectively applied to intelligent transportation and realize intelligent management of traffic. The use of representative vehicle signal features is a prerequisite and guarantee for accurate vehicle classification. This paper uses a single 3-axis magnetic sensor to acquire vehicle signals and extract a large number of features from the vehicle signals. In order to obtain a set of features that are simple and without loss of classification accuracy, we propose a Filtering algorithm based on Feature Pairing Elimination (FPE-Filter). In addition, choosing the right classifier is also crucial for models with high classification accuracy. In this paper, we compare four common classification models: SVM, RF, KNN, and C4.5. The experimental results show that the SVM performs best and the classification accuracy reaches 95%.