Big Data Analysis for Media Production

Josep Blat, Alun Evans, Hansung Kim, Evren Imre, Lukas Polok, Viorela Ila, Nikos Nikolaidis, Pavel Zemcik, Anastasios Tefas, Pavel Smrz, Adrian Hilton, Ioannis Pitas
<span title="">2016</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yfvtieuumfamvmjlc255uckdlm" style="color: black;">Proceedings of the IEEE</a> </i> &nbsp;
A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). This paper presents solutions to improve the integration of the multiple data sources, and understand their quality and content, which are useful both to support creative decisions on-set (or near it) and enhance the post-production process. The main cinema specific contributions, tested on
more &raquo; ... multisource production dataset made publicly available for research purposes, are the monitoring and quality assurance of multi-camera set-ups, multisource registration and acceleration of 3D reconstruction, anthropocentric visual analysis techniques for semantic content annotation, and integrated 2D-3D web visualization tools. We discuss as well improvements carried out in basic techniques for acceleration, clustering and visualization, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jproc.2015.2496111">doi:10.1109/jproc.2015.2496111</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u5cy6uv3bbfvjebuww7x4b3yfm">fatcat:u5cy6uv3bbfvjebuww7x4b3yfm</a> </span>
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