Automatic Live Sport Video Streams Curation System from User Generated Media

Automatic Live Sport Video Streams Curation System from User Generated Media

Kazuki Fujisawa, Yuko Hirabe, Hirohiko Suwa, Yutaka Arakawa, Keiichi Yasumoto
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJMDEM.2016040103
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Abstract

Emerging Internet of Things (IoT) technologies will allow spectators in a sport game to produce various video streams from various angles. With existing technologies, however, it is difficult to process massive and various data streams for multi-channel contents in real-time. To solve this problem, we aim to construct a software agent (called “Curator”) that compiles video contents automatically according to his/her values. In this paper, we propose a system to automatically switch multiple video streams that general sports spectators have taken using Random Forests classifier. Meta data such as image feature data and game progress data is extracted for each video scene as the input of the classifier. For evaluation, we constructed a camera switching timing estimation model using the live TV broadcast of some baseball game data. A video of another baseball game was curated with the constructed model. As a result, our system predicted the camera switching timing with accuracy (F-measure) of 85.3% on weighted average for the base camera work and 99.7% for the fixed camera work.
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In this section, existing studies are first explained on content curation to show the difference from our proposed method. Then, other related studies are described such as automatic camera control and pattern recognition for sports video and automatic live-sport broadcast summarization.

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