Moving vehicle identification using artificial neural networks

Identification of moving vehicle type and its position using an acoustic sound signal is a critical task in many applications such as tactile sensory aid for differentially hearing ability abled, war field surveillance, border monitoring, traffic control systems. Moreover a wide range of research ha...

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Main Author:	Sathishkumar, Sundararaj
Format:	Thesis
Language:	English
Subjects:	Vehicle identification Neural network Acoustic sound signal Artificial Neural Network
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/2/full%20text.pdf
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id	my-unimap-44110
record_format	uketd_dc
spelling	my-unimap-441102016-11-18T12:01:30Z Moving vehicle identification using artificial neural networks Sathishkumar, Sundararaj Identification of moving vehicle type and its position using an acoustic sound signal is a critical task in many applications such as tactile sensory aid for differentially hearing ability abled, war field surveillance, border monitoring, traffic control systems. Moreover a wide range of research has been done for moving vehicle type identification using different signal processing techniques and algorithms. Differ from the previous researches this work intends to identify the moving vehicle type and its position from the subject. This research work is mainly divided into three sections. The first section deals with the state of the art review of various methods used to identify moving vehicles. The second section describes the experimental protocol design and feature extraction methods. Third section explains the classification of moving vehicles using Artificial Neural Network. A simple experimental protocol has been developed to record the moving vehicle sound signal using Sony recorder ICD-SX700. The recorded sound signals are preprocessed using the windowing technique. Feature extraction methods are based on two different approaches such as time domain and frequency domain. For time domain feature extraction, Autoregressive (AR) model and statistical features such as energy, kurtosis, standard deviation and skweness are used. For frequency domain, the signal is preprocessed using designed Butterworth filter with the band frequencies of 1/3 octave. The time domain signal is then transformed into frequency domain using Discrete Fourier Transform. The extracted features are then normalized and randomized to rearrange the values into definite range. Neural network models are used for the classification of moving vehicles. Network model such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Elman Neural Network (ENN) and Probabilistic Neural Network (PNN) are modelled. From the evaluation for the vehicle type and position classification, it can be inferred that the PNN model developed using the spectral band features has better classification accuracy while compared to other models. The performances of the networks are calculated and the results are compared. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44110 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/1/p.1-24.pdf 0c9df420824168f360a316cf4ad26243 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/2/full%20text.pdf 03b06c15065824a07135860a246d98f3 Vehicle identification Neural network Acoustic sound signal Artificial Neural Network School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Vehicle identification Neural network Acoustic sound signal Artificial Neural Network
spellingShingle	Vehicle identification Neural network Acoustic sound signal Artificial Neural Network Sathishkumar, Sundararaj Moving vehicle identification using artificial neural networks
description	Identification of moving vehicle type and its position using an acoustic sound signal is a critical task in many applications such as tactile sensory aid for differentially hearing ability abled, war field surveillance, border monitoring, traffic control systems. Moreover a wide range of research has been done for moving vehicle type identification using different signal processing techniques and algorithms. Differ from the previous researches this work intends to identify the moving vehicle type and its position from the subject. This research work is mainly divided into three sections. The first section deals with the state of the art review of various methods used to identify moving vehicles. The second section describes the experimental protocol design and feature extraction methods. Third section explains the classification of moving vehicles using Artificial Neural Network. A simple experimental protocol has been developed to record the moving vehicle sound signal using Sony recorder ICD-SX700. The recorded sound signals are preprocessed using the windowing technique. Feature extraction methods are based on two different approaches such as time domain and frequency domain. For time domain feature extraction, Autoregressive (AR) model and statistical features such as energy, kurtosis, standard deviation and skweness are used. For frequency domain, the signal is preprocessed using designed Butterworth filter with the band frequencies of 1/3 octave. The time domain signal is then transformed into frequency domain using Discrete Fourier Transform. The extracted features are then normalized and randomized to rearrange the values into definite range. Neural network models are used for the classification of moving vehicles. Network model such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Elman Neural Network (ENN) and Probabilistic Neural Network (PNN) are modelled. From the evaluation for the vehicle type and position classification, it can be inferred that the PNN model developed using the spectral band features has better classification accuracy while compared to other models. The performances of the networks are calculated and the results are compared.
format	Thesis
author	Sathishkumar, Sundararaj
author_facet	Sathishkumar, Sundararaj
author_sort	Sathishkumar, Sundararaj
title	Moving vehicle identification using artificial neural networks
title_short	Moving vehicle identification using artificial neural networks
title_full	Moving vehicle identification using artificial neural networks
title_fullStr	Moving vehicle identification using artificial neural networks
title_full_unstemmed	Moving vehicle identification using artificial neural networks
title_sort	moving vehicle identification using artificial neural networks
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44110/2/full%20text.pdf
_version_	1747836821479358464

Moving vehicle identification using artificial neural networks

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