Generation of Personalized Music Sports Video Using Multimodal Cues
In this paper, we propose a novel automatic approach for personalized music sports video generation. Two research challenges are addressed, specifically the semantic sports video content extraction and the automatic music video composition. For the first challenge, we propose to use multimodal (audio, video, and text) feature analysis and alignment to detect the semantics of events in broadcast sports video. For the second challenge, we introduce the video-centric and music-centric music video composition schemes and proposed a dynamic-programming based algorithm to perform fully or semi-automatic generation of personalized music sports video. The experimental results and user evaluations are promising and show that our systems generated music sports video is comparable to professionally generated ones. Our proposed system greatly facilitates the music sports video editing task for both professionals and amateurs