Pattern classification of human interactions from videos / Muhsin Abdul Mohammed

The objective of this research project is to build a machine learning model to classify human interactions from a stream of video. Being able to classify human interaction from videos is essential in the development of robotic assistance systems, video annotation, surveillance systems and many m...

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Main Author: Muhsin , Abdul Mohammed
Format: Thesis
Published: 2018
Subjects:
Online Access:http://studentsrepo.um.edu.my/9627/1/Muhsin_Abdul_Mohammed.jpg
http://studentsrepo.um.edu.my/9627/8/muhsin.pdf
http://studentsrepo.um.edu.my/9627/
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author Muhsin , Abdul Mohammed
author_facet Muhsin , Abdul Mohammed
author_sort Muhsin , Abdul Mohammed
building UM Library
collection Institutional Repository
content_provider Universiti Malaya
content_source UM Student Repository
continent Asia
country Malaysia
description The objective of this research project is to build a machine learning model to classify human interactions from a stream of video. Being able to classify human interaction from videos is essential in the development of robotic assistance systems, video annotation, surveillance systems and many more applications. It is necessary that the algorithm performing this task needs to be robust and only relies on monocular vision systems. In order to build a classifier capable of achieving this task, the machine learning model needs to be able to learn spatial and temporal patterns from the videos. A cascaded architecture of Convolutional Neural Networks and Recurrent Neural Networks have been created to achieve this task in this research. There have been investigations made to identify the best spatial and temporal architectures that would give the optimal result.
format Thesis
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institution Universiti Malaya
publishDate 2018
record_format eprints
spelling my.um.stud-96272021-04-11T23:21:09Z Pattern classification of human interactions from videos / Muhsin Abdul Mohammed Muhsin , Abdul Mohammed T Technology (General) The objective of this research project is to build a machine learning model to classify human interactions from a stream of video. Being able to classify human interaction from videos is essential in the development of robotic assistance systems, video annotation, surveillance systems and many more applications. It is necessary that the algorithm performing this task needs to be robust and only relies on monocular vision systems. In order to build a classifier capable of achieving this task, the machine learning model needs to be able to learn spatial and temporal patterns from the videos. A cascaded architecture of Convolutional Neural Networks and Recurrent Neural Networks have been created to achieve this task in this research. There have been investigations made to identify the best spatial and temporal architectures that would give the optimal result. 2018-07 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/9627/1/Muhsin_Abdul_Mohammed.jpg application/pdf http://studentsrepo.um.edu.my/9627/8/muhsin.pdf Muhsin , Abdul Mohammed (2018) Pattern classification of human interactions from videos / Muhsin Abdul Mohammed. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/9627/
spellingShingle T Technology (General)
Muhsin , Abdul Mohammed
Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title_full Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title_fullStr Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title_full_unstemmed Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title_short Pattern classification of human interactions from videos / Muhsin Abdul Mohammed
title_sort pattern classification of human interactions from videos / muhsin abdul mohammed
topic T Technology (General)
url http://studentsrepo.um.edu.my/9627/1/Muhsin_Abdul_Mohammed.jpg
http://studentsrepo.um.edu.my/9627/8/muhsin.pdf
http://studentsrepo.um.edu.my/9627/
url_provider http://studentsrepo.um.edu.my/