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An Improve Shannon Fano Data Compression Algorithm using Residue Number System

T.D. Lawal L.O. Olatunbosun K.A. Gbolagade
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Intelligent Vehicular Traffic Light Control using Hidden Markov Model

by Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 5
Year of Publication: 2017
Authors: Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong
10.5120/cae2017652668

Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong . Intelligent Vehicular Traffic Light Control using Hidden Markov Model. Communications on Applied Electronics. 7, 5 ( Aug 2017), 12-20. DOI=10.5120/cae2017652668

@article{ 10.5120/cae2017652668,
author = { Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong },
title = { Intelligent Vehicular Traffic Light Control using Hidden Markov Model },
journal = { Communications on Applied Electronics },
issue_date = { Aug 2017 },
volume = { 7 },
number = { 5 },
month = { Aug },
year = { 2017 },
issn = { 2394-4714 },
pages = { 12-20 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number5/757-2017652668/ },
doi = { 10.5120/cae2017652668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:01:31.871500+05:30
%A Dominic Asamoah
%A Samuel Winful
%A Stephen Opoku Oppong
%T Intelligent Vehicular Traffic Light Control using Hidden Markov Model
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 5
%P 12-20
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traffic management continues to remains a major problem in big cities. Allocating limited resources, i.e. roads, to an increasing number of users with individual needs and objectives, turns out to be a highly complex in most cases. This research uses Hidden Markov Model (HMM) as a component with unsupervised clustering scheme to determine the traffic situation of a road in a traffic video. An unsupervised clustering algorithm called Autoclass is applied to obtain the traffic density state (free, normal and congested) on motion features which are extracted from each frame. The three HMM models are constructed for each traffic state with each cluster corresponding to a state in the HMM. The result show that this method can handle varying illumination and classify traffic density in a (Region of Interest) ROI of a traffic video.

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Index Terms

Computer Science
Information Sciences

Keywords

Traffic management unsupervised clustering Hidden Markov Model Autoclass traffic density

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