Vehicle Classification Based on Magnetic Sensor Signal
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 lowcomplexity 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.
vehicle classification magnetic sensor classification tree
Saowaluck Kaewkamnerd Jatuporn Chinrungrueng Ronachai Pongthornseri Songphon Dumnin
National Electronics and Computer Technology CenterThailand Science Park,Pathumthani,Thailand National Electronics and Computer Technology Center Thailand Science Park,Pathumthani,Thailand
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-5
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）