LAUSR

This paper presents a road vehicle recognition and classification approach for intelligent transportation systems. This approach uses a roadside installed low-cost magnetometer and associated data collection system. The system measures the magnetic field changing, detects passing vehicles, and recognizes vehicle types. We introduce Mel Frequency Cepstral Coefficients (MFCC) to analyze vehicle magnetic signals and extract it as vehicle feature with the representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 3-dimensional map algorithm using Vector Quantization (VQ) to classify vehicle magnetic features to 4 typical types of vehicles in Australian suburbs: sedan, van, truck, and bus. In order to train an accurate classifier, training samples are selected using the Dynamic Time Warping (DTW). The verification experiments show that our approach achieves a high level of accuracy for vehicle detection and classification.