VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS

This report shows a study on orientation robust vehicle classification for data analytics purposes by using (HOG) feature set. The study is conducted to analyse the performance of histograms of oriented gradient(HOG) between Linear SVM algorithm and Aggregated Channel Features (ACF) algorithm which...

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Main Author: KAMARUDDIN, ERNA SHAFFIQA
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.19164 /
Published: 2018
Online Access: http://utpedia.utp.edu.my/19164/1/FINAL%20REPORT_20782.pdf
http://utpedia.utp.edu.my/19164/
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id utp-utpedia.19164
recordtype eprints
spelling utp-utpedia.191642019-06-20T10:41:17Z http://utpedia.utp.edu.my/19164/ VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS KAMARUDDIN, ERNA SHAFFIQA This report shows a study on orientation robust vehicle classification for data analytics purposes by using (HOG) feature set. The study is conducted to analyse the performance of histograms of oriented gradient(HOG) between Linear SVM algorithm and Aggregated Channel Features (ACF) algorithm which is using Decision Trees. The method and experimental design is shown to demonstrate the good accuracy for detecting and classifying object with various angle. The feature extraction obtain is trained using Linear SVM and Decision Trees. The result shows that the Linear SVM algorithm outperform ACF algorithm with accuracy percentage of 88.5% compared to 62.8% for smaller datasets. 2018-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19164/1/FINAL%20REPORT_20782.pdf KAMARUDDIN, ERNA SHAFFIQA (2018) VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS. UNSPECIFIED.
institution Universiti Teknologi Petronas
collection UTPedia
language English
description This report shows a study on orientation robust vehicle classification for data analytics purposes by using (HOG) feature set. The study is conducted to analyse the performance of histograms of oriented gradient(HOG) between Linear SVM algorithm and Aggregated Channel Features (ACF) algorithm which is using Decision Trees. The method and experimental design is shown to demonstrate the good accuracy for detecting and classifying object with various angle. The feature extraction obtain is trained using Linear SVM and Decision Trees. The result shows that the Linear SVM algorithm outperform ACF algorithm with accuracy percentage of 88.5% compared to 62.8% for smaller datasets.
format Final Year Project
author KAMARUDDIN, ERNA SHAFFIQA
spellingShingle KAMARUDDIN, ERNA SHAFFIQA
VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
author_sort KAMARUDDIN, ERNA SHAFFIQA
title VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
title_short VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
title_full VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
title_fullStr VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
title_full_unstemmed VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS
title_sort vehicle classification from cctv images and videos
publishDate 2018
url http://utpedia.utp.edu.my/19164/1/FINAL%20REPORT_20782.pdf
http://utpedia.utp.edu.my/19164/
_version_ 1741195458184740864
score 11.62408

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