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.

Automatic Route Video Summarization based on Image Analysis for Intuitive Touristic Experience

“…Existing methods applied to news programs (14) and sports programs (15) detect important scenes easily by utilizing the fact that these videos have apparent scene changes with switching among multiple cameras or characteristic sounds (e.g., before switching to a new report). Many existing methods for summarizing these videos exist.…”

“…Many existing methods for summarizing these videos exist. (14,15,18,19) However, we cannot use these methods because the tour videos taken as CGM are typically one continuous shot and do not have apparent scene changes (camera switching) or characteristic sounds (between scene changes).…”

“…There are many studies of video summarization allowing users to watch a video quickly and efficiently. (14,15) When summarizing a video, extracting scenes by characteristic frames or sounds and calculating the importance of these scenes are general processes. In particular, news (14) and sports programs (15) have scene changes with multiple cameras or characteristic sounds, and important scenes in these videos can be clearly identified.…”

“…(14,15) When summarizing a video, extracting scenes by characteristic frames or sounds and calculating the importance of these scenes are general processes. In particular, news (14) and sports programs (15) have scene changes with multiple cameras or characteristic sounds, and important scenes in these videos can be clearly identified. Sightseeing videos were taken by tourists; however, they do not include featured scene switching or sounds unlike news programs or sports videos.…”