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Vehicle Classification Based on Magnetic Sensor Signal

机译：基于磁传感器信号的车辆分类

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We extend our work in vehicle classification proposed in [6] and [7]. Our system is based on a low complexity wireless sensor network. The system consists of a low power microprocessor together with AMR magnetic sensors and an RF transceiver. Two AMR magnetic sensors are employed to extracts dominant low-complexity features including vehicle count, speed, length, Hill-pattern peaks, and normalized energy. These features are studied in [6] and [7] and yield a promising result when vehicle classification is based on sizes (96%). However, when classification of similar sizes, e.g. cars, vans, pickup trucks are studied. The results are relatively lower at 77%. The contribution of this paper include (1) the implementation of feature extraction (count, speed, length) on sensor board and (2) the study for additional different low-complexity features such that better classification rate of small vehicles is obtained. These features include Hill-pattern peaks and magnetic signal differential energy normalized to the vehicle speed and length. This paper proposed vehicle classification tree based on above extraction features. Our work focuses on low computational feature extraction and classification processes suitable for implementing on micro-controller. The same data set employed in [7] is analyzed. The classification yields promising improved results over [6] and [7]. The classification rate yield 100 percent for motorcycle, 82.46 percent for car, 78.57 percent for van and 65.71 percent for pickup. The overall accuracy is 81.69 percent.
机译：我们在[6]和[7]中提出的车辆分类中扩展了我们的工作。我们的系统基于低复杂性无线传感器网络。该系统由低功耗微处理器与AMR磁传感器和RF收发器组成。使用两种AMR磁传感器以提取包括车辆数，速度，长度，山丘图案峰和归一化能量的显性低复杂性特征。在[6]和[7]中研究了这些特征，并在车辆分类基于大小（96％）时产生有希望的结果。然而，当类似尺寸的分类时，例如汽车，面包车，皮卡搬运。结果相对较低77％。本文的贡献包括（1）传感器板上的特征提取（计数，速度，长度）的实施​​和（2）对额外的不同低复杂性特征的研究，使得获得更好的小型车辆的分类率。这些特征包括山丘模式峰值和磁信号差动能量，归一化到车速和长度。本文提出了基于上述提取特征的车辆分类树。我们的工作侧重于适合于在微控制器上实施的低计算功能提取和分类过程。分析[7]中使用的相同数据集。分类产生的促进结果提高了[6]和[7]。摩托车的分类率为100％，汽车82.46％，面包车的78.57％，拾取的65.71％。整体准确性为81.69％。

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