To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information. Previous work has successfully extracted semantic from the time-stamped web match reports, which are synchronized with the video contents. However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments. This paper aims to address this limitation using a novel multimodal summarization framework that is based on sentiment analysis and players' popularity. It uses audiovisual contents, web articles, blogs, and commentators' speech to automatically annotate and visualize the key events and key players in a sports tournament coverage. The experimental results demonstrate that the automatically generated video summaries are aligned with the events identified from the official website match reports.
Tjondronegoro et al. REF presents a framework for video summarization to automatically detect and mark the key events and key players in broadcasted sports video.
Multi-modal summarization of key events and top players in sports tournament videos
2011 IEEE Workshop on Applications of Computer Vision (WACV)
2011 IEEE Workshop on Applications of Computer Vision (WACV)
Computer Science