Paper Info
Title
Automated Whiteboard Lecture Video Summarization By Content Region Detection And Representation
Abstract
Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9412386
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
DocType
ISSN
Citations 
Conference
1051-4651
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Bhargava Urala Kota102.03
Alexander Stone200.34
Kenny Davila3276.03
Srirangaraj Setlur426730.66
Venu Govindaraju53521422.00