Automatic Live Sport Video Streams Curation System From User Generated Media

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.