Detecting inactivity segments in E-learning video recordings (DISE)
Other Title(s)
تحديد المقاطع غير النشطة في فيديوهات المحاضرات في التعليم الإلكتروني
Dissertant
Thesis advisor
University
Islamic University
Faculty
Faculty of Information Technology
University Country
Palestine (Gaza Strip)
Degree
Master
Degree Date
2015
English Abstract
Lecture video recordings used in e-learning typically contain segments of inactivity which are better removed in order to save storage and bandwidth load, and to decrease viewing time and effort.
This problem is also common in other areas such as multimedia.
This research proposes a method for detecting segments of inactivity in video sequences and marking them for further processing.
The method is known as DISE.
The core of our approach are two modules one for sound inactivity detection and the other for video inactivity detection.
Inactivity segments detected by the sound module are forwarded to the video module for video inactivity processing.
The method has been developed into a system (DISE) and tested using a collection of real lecture videos obtained from lecture recordings at University of Palestine for different courses under various recording conditions.
The method performs close to human detection as it achieves accuracy of 84.4%.
We believe that our method will add value to the process of recording lectures in the e-learning domain in educational institutes .
Main Subjects
Educational Sciences
Information Technology and Computer Science
Topics
No. of Pages
84
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Related works.
Chapter Four : Methodology and proposed method.
Chapter Five : Experimental results and evaluation.
Chapter Six : Conclusion and future work.
References.
American Psychological Association (APA)
Hammu, Sahar S.. (2015). Detecting inactivity segments in E-learning video recordings (DISE). (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-688588
Modern Language Association (MLA)
Hammu, Sahar S.. Detecting inactivity segments in E-learning video recordings (DISE). (Master's theses Theses and Dissertations Master). Islamic University. (2015).
https://search.emarefa.net/detail/BIM-688588
American Medical Association (AMA)
Hammu, Sahar S.. (2015). Detecting inactivity segments in E-learning video recordings (DISE). (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-688588
Language
English
Data Type
Arab Theses
Record ID
BIM-688588
