Road Vehicle Detection and Classification Using Magnetic Field Measurement

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 Dynamic Time Warping (DTW). Verification experiments show that our approach achieves a high level of accuracy for vehicle detection and classification.

本论文提出一种面向智能交通系统的道路车辆识别与分类方法。该方法采用路边部署的低成本磁强计及配套数据采集系统，通过监测磁场变化实现过往车辆的检测与车型识别。我们引入梅尔频率倒谱系数（Mel Frequency Cepstral Coefficients, MFCC）分析车辆磁信号，并以磁信号的倒谱、帧能量及间隙倒谱作为车辆特征进行提取。我们设计了一种基于矢量量化（Vector Quantization, VQ）的三维映射算法，将车辆磁特征划分为澳大利亚城郊常见的四类典型车型：轿车、厢式货车、卡车与巴士。为训练高精度分类器，我们采用动态时间规整（Dynamic Time Warping, DTW）选取训练样本。验证实验结果表明，所提方法在车辆检测与分类任务中可达到较高的准确率。