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Structure and event mining in sports video with efficient mosaic

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Abstract

Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a mosaic based approach to key-event as well as structure mining, which is regarded as a complementary view for sports video analysis. Mosaic is generated for each shot by a novel efficient mosaicing scheme, which constructs a global motion path and selects a best subset of frames for mosaicing. These improved mosaics are then used as the representative image of shot content. Based on mosaic, the structure and event in sports video are mined by the methods with prior knowledge and without prior knowledge. Without prior knowledge, our system is able to locate global view shots taken by dominant camera. If prior knowledge is available, the events in these global view shots are detected using robust features extracted from mosaics. For global view mining, the experiments compared with key-frame-based scheme have demonstrated that this mosaic-based scheme presents better results in several kinds of sports videos; for events mining, the detection of key-plays and key-events in the specific-domain of soccer videos have proved its effectiveness.

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Authors and Affiliations

  1. Microsoft Research Asia, Beijing, 100080, People’s Republic of China

    Tao Mei & Xian-Sheng Hua

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  1. Tao Mei
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  2. Xian-Sheng Hua
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Correspondence to Tao Mei.

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Mei, T., Hua, XS. Structure and event mining in sports video with efficient mosaic. Multimed Tools Appl 40, 89–110 (2008). https://doi.org/10.1007/s11042-007-0186-8

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