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InVideo: An Automatic Video Index and Search Engine for Large Video Collections

Authors:
Shuangbao Paul Wang
Xiaolong Cheng
Carolyn Maher
William Kelly

Keywords: video processing; video index; big data; learning analytics.

Abstract:
In this paper, we present a novel system, inVideo, for automatically indexing and searching videos based on the keywords spoken in the videos and the content of the video frames. Using the highly efficient video indexing engine we developed, InVideo is able to analyze videos using machine learning and pattern recognition without the need for initial viewing by a human. The time-stamped commenting and tagging features refine the accuracy of search results. The cloud-based implementation makes it possible to conduct elastic search, augmented search, and data analytics. Our research shows that inVideo presents an efficient tool in processing and analyzing videos and increasing interactions in video-based online learning environment. Data from a cybersecurity program with more than 500 students show that applying inVideo to current video material, interactions between student-student and student-faculty increased significantly across 24 sections program-wide.

Pages: 27 to 32

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2519-8432

ISBN: 978-1-61208-559-3

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017