Automatic vehicle classification using wireless magnetic sensor

Kaewkamnerd, Saowaluck; Pongthornseri, Ronachai; Chinrungrueng, Jatuporn; Silawan, Teerapol

doi:10.1109/idaacs.2009.5342949

Cited by 26 publications

(23 citation statements)

References 2 publications

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“…The signal transmission is finished when the vehicle is departed. It was shown in [6] and [7] that vehicle length estimation can be employed and yield classification of small vehicles from larger ones very well. The system computes the following parameters on board: vehicle speed and length estimation.…”

Section: Feature Extractionmentioning

confidence: 99%

“…L d is calculated based on the two-stage scheme [7]. In first stage, estimated vehicle length is obtained by number of delay sample between two magnetic sensors.…”

Section: A Relative Vehicle Lengthmentioning

confidence: 99%

“…After the vehicle is detected, all these parameters and data are transmitted to a personal computer for feature extraction and classification processes. The same data employed in [7] is analyzed so the results can be compared. The features used in 935 978-1-4244-5704-5/10/$26.00 ©2010 IEEE Proceedings of the 2010 IEEE International Conference on Information and Automation June 20 -23, Harbin, China classification system are relative vehicle length, signal length, number of Hill-pattern peaks and differential energy parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose an extension to the classification system in [6] and [7]. The sensor we used is two AMR sensors installed on the sensor board.…”

Section: Introductionmentioning

confidence: 99%

“…Their system employed several sensors including video camera, infrared and piezoelectric sensor with high level of complexity. In [6] and [7], a vehicle classification system was proposed based on a wireless sensor network. Two AMR sensors are deployed for vehicle detection and extraction of various parameters such as speed, vehicle length and vehicle magnetic energy.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle classification based on magnetic sensor signal

Kaewkamnerd

Chinrungrueng

Pongthornseri

et al. 2010

The 2010 IEEE International Conference on Information and Automation

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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 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.

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Section: Feature Extractionmentioning

confidence: 99%

“…L d is calculated based on the two-stage scheme [7]. In first stage, estimated vehicle length is obtained by number of delay sample between two magnetic sensors.…”

Section: A Relative Vehicle Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose an extension to the classification system in [6] and [7]. The sensor we used is two AMR sensors installed on the sensor board.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle classification based on magnetic sensor signal

Kaewkamnerd

Chinrungrueng

Pongthornseri

et al. 2010

The 2010 IEEE International Conference on Information and Automation

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Improved waveform‐feature‐based vehicle classification using a single‐point magnetic sensor

Sun

et al. 2014

J of Advced Transportation

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Summary Vehicle classification systems have important roles in applications related to real‐time traffic management. They also provide essential data and necessary information for traffic planning, pavement design, and maintenance. Among various classification techniques, the length‐based classification technique is widely used at present. However, the undesirable speed estimates provided by conventional data aggregation make it impossible to collect reliable length data from a single‐point sensor during real‐time operations. In this paper, an innovative approach of vehicle classification will be proposed, which achieved very satisfactory results on a single‐point sensor. This method has two essential parts. The first concerns with the procedure of smart feature extraction and selection according to the proposed filter–filter–wrapper model. The model of filter–filter–wrapper is adopted to make an evaluation on the extracted feature subsets. Meanwhile, the model will determine a nonredundant feature subset, which can make a complete reflection on the differences of various types of vehicles. In the second part, an algorithm for vehicle classification according to the theoretical basis of clustering support vector machines (C‐SVMs) was established with the selected optimal feature subset. The paper also uses particle swarm optimization (PSO), with the purpose of searching for an optimal kernel parameter and the slack penalty parameter in C‐SVMs. A total of 460 samples were tested through cross validation, and the result turned out that the classification accuracy was over 99%. In summary, the test results demonstrated that our vehicle classification method could enhance the efficiency of machine‐learning‐based data mining and the accuracy of vehicle classification. Copyright © 2014 John Wiley & Sons, Ltd.

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Reliable vehicle type recognition based on information fusion in multiple sensor networks

2017

Computer Networks

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Automatic vehicle classification using wireless magnetic sensor

Cited by 26 publications

References 2 publications

Vehicle classification based on magnetic sensor signal

Vehicle classification based on magnetic sensor signal

Improved waveform‐feature‐based vehicle classification using a single‐point magnetic sensor

Reliable vehicle type recognition based on information fusion in multiple sensor networks

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