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Vol. 215, Issue 8, August 2017, pp. 27-34




Automated Indexing and Search of Video Data in Large Collections with inVideo

1 Shuangbao Paul Wang, 2 Xialong Cheng, 3 Carolyn Maher, 4 William Kelly

1 Metonymy Corporation, Fairfax, Virginia, 22030, USA
2 George Washington University, Washington, DC, 20052, USA
3 Rutgers University, New Brunswick, NJ, 08901, USA
4 Metonymy Corporation, Fairfax, Virginia, 22030, USA
1 Tel.: 703.628.7936

1 E-mail: paul.wang@computer.org


Received: 1 June 2017 /Accepted: 7 August 2017 /Published: 31 August 2017

Digital Sensors and Sensor Sysstems


Abstract: In this paper, we present a novel system, inVideo, for automatically indexing and searching videos based on the keywords spoken in the audio track and the visual 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.


Keywords: Video processing, Video index, Big data, Learning analytics, Education


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This work is licensed under a Creative Commons 4.0 International License


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