Automated Vehicle Counting And Classification System

This paper describes a system that can detect, count and classify vehicles automatically based on their types. Some techniques have been conducted in this paper to achieve the objectives of the project. The detection of the vehicles has been done by using background subtraction. The image produced f...

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Main Author: Afandi, Abdul Muiz
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.19129 /
Published: 2017
Online Access: http://utpedia.utp.edu.my/19129/1/FINAL%20DISSERTATION.pdf
http://utpedia.utp.edu.my/19129/
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id utp-utpedia.19129
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spelling utp-utpedia.191292019-06-20T11:06:36Z http://utpedia.utp.edu.my/19129/ Automated Vehicle Counting And Classification System Afandi, Abdul Muiz This paper describes a system that can detect, count and classify vehicles automatically based on their types. Some techniques have been conducted in this paper to achieve the objectives of the project. The detection of the vehicles has been done by using background subtraction. The image produced from background subtraction will be improved by using foreground detection to get better quality of image. Another approach that can be used is by using object segmentation technique which will create a bounding box for each vehicle to increase the accuracy of vehicles detection. On the other hand, vehicles detection can be done by using virtual detection line (VDL) which will be placed perpendicularly to the motion of vehicles. By implementing those methods, the vehicles can be detected and counted automatically, and the classification process of them will be done based on vehicle’s length and width. The results obtained by the system will be compared to the original situation to get higher accuracy rate. The higher accuracy rate will lead to the successful system that can detect, count and classify vehicles automatically. 2017-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19129/1/FINAL%20DISSERTATION.pdf Afandi, Abdul Muiz (2017) Automated Vehicle Counting And Classification System. UNSPECIFIED.
institution Universiti Teknologi Petronas
collection UTPedia
language English
description This paper describes a system that can detect, count and classify vehicles automatically based on their types. Some techniques have been conducted in this paper to achieve the objectives of the project. The detection of the vehicles has been done by using background subtraction. The image produced from background subtraction will be improved by using foreground detection to get better quality of image. Another approach that can be used is by using object segmentation technique which will create a bounding box for each vehicle to increase the accuracy of vehicles detection. On the other hand, vehicles detection can be done by using virtual detection line (VDL) which will be placed perpendicularly to the motion of vehicles. By implementing those methods, the vehicles can be detected and counted automatically, and the classification process of them will be done based on vehicle’s length and width. The results obtained by the system will be compared to the original situation to get higher accuracy rate. The higher accuracy rate will lead to the successful system that can detect, count and classify vehicles automatically.
format Final Year Project
author Afandi, Abdul Muiz
spellingShingle Afandi, Abdul Muiz
Automated Vehicle Counting And Classification System
author_sort Afandi, Abdul Muiz
title Automated Vehicle Counting And Classification System
title_short Automated Vehicle Counting And Classification System
title_full Automated Vehicle Counting And Classification System
title_fullStr Automated Vehicle Counting And Classification System
title_full_unstemmed Automated Vehicle Counting And Classification System
title_sort automated vehicle counting and classification system
publishDate 2017
url http://utpedia.utp.edu.my/19129/1/FINAL%20DISSERTATION.pdf
http://utpedia.utp.edu.my/19129/
_version_ 1741195453392748544
score 11.62408

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