Automating Metadata Logging through Artificial Intelligence

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2018, No. 10, pp. 1–10
Abstract: In 2007 NASCAR designed and implemented a media asset management solution for their sport. Now, in 2018, the library has grown to more than 500,000 hours of content containing video, audio and still images dating back to 1933 — one of the most vast sports libraries in the World. — Since the inception of the library, NASCAR has employed a staff to manually apply metadata to each video frame, amassing 10 million entries. While the logging by our staff has been impressive, the incoming data avalanche cannot be addressed by using the current system, let alone the glacier of data hiding in the archive. At present, we have calculated that it would take our existing staff nearly 150 years to log all of the historical content as it stands today. Over the past ten years we have extensively analyzed open-source and proprietary tools aimed at dealing with the data logging gap and have determined Machine Learning is the ideal solution to address metadata logging on large-scale media libraries. — As Machine Learning has become more accessible through the scalability of cloud computing, training and implementing Convolutional Neural Networks is now within the reach of media production companies and asset stakeholders. NASCAR is on the verge of revolutionizing how all data asset management systems can be restructured in the future to integrate machine learning to harness efficiencies in metadata logging.
Publication Date: 2018-10-22
DOI: 10.5594/M001814
ISBN: Print: 978-1-61482-960-7
Link: https://doi.org/10.5594/M001814
Author(s): Christopher WitmayerNASCAR
Keyword(s): Artificial Intelligence, CNN, Machine Learning, Metadata Tagging, Video Logging, Tensorflow, Visual Field, Transfer Learning
Copyright: © 2018 Society of Motion Picture and Television Engineers, Inc.

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Christopher Witmayer; Automating Metadata Logging through Artificial Intelligence, SMPTE Meetings and Conferences ( October 2018); SMPTE, 2018, ISBN: 978-1-61482-960-7. Available at https://doi.org/10.5594/M001814

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Christopher Witmayer; Automating Metadata Logging through Artificial Intelligence, SMPTE Meetings and Conferences ( October 2018); SMPTE, 2018, ISBN: 978-1-61482-960-7. Available at https://doi.org/10.5594/M001814

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<span class="citation">Christopher Witmayer; <cite>Automating Metadata Logging through Artificial Intelligence</cite>, SMPTE Meetings and Conferences ( October 2018); SMPTE, 2018, ISBN: 978-1-61482-960-7. Available at <a href="https://doi.org/10.5594/M001814" target="_blank" rel="noopener">https://doi.org/10.5594/M001814</a></span>

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Christopher Witmayer; Automating Metadata Logging through Artificial Intelligence, SMPTE Meetings and Conferences ( October 2018); SMPTE, 2018, ISBN: 978-1-61482-960-7
doi: 10.5594/M001814
url: https://doi.org/10.5594/M001814

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<li>
Christopher Witmayer; <cite id="bib-10-5594-m001814">Automating Metadata Logging through Artificial Intelligence</cite>, SMPTE Meetings and Conferences ( October 2018); SMPTE, 2018, ISBN: 978-1-61482-960-7
<span class="doi">10.5594/M001814</span>
</li>