A framework for extracting sports video highlights using social media

Abstract

Summarizing lengthy sports video into compact highlights has many applications and plays an essential role for effective media dissemination and delivery. To perform the highlights extraction correctly and effectively is of great challenge. Extensive research efforts have been made to this problem in recent years. In practice, sports video highlights are extracted either manually or based on video content analysis schemes. The former approach is not cost effective and naturally brings the scalability concern, while the later approach suffers from high computational complexity. In this paper, we start from a novel angle to address the sports video summarization problem; we employ real-time text stream, e.g. opinion comment posts, from social media to detect events and the event semantics in live sport videos. The main idea is that one can treat the volumes of comment posts over time as a time series, and the variation of the time series, such as a spike, may reveal events in a game, which therefore can be employed to identify the important moments in the game. By aligning the events with the sports videos over time, automatically summarizing sports video may be feasible. This paper describes the implementation of this idea and reports our experience of summarizing the 2014 World Cup Video. We also evaluate our technique compared to human-generated summaries and find that the results of our technique are quite similar to the human-generated result, which demonstrate the superiority of our technique.