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Highlights Extraction in Sports Videos Based on Automatic Posture and Gesture Recognition

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

Content-based indexing of sports videos is usually based on the automatic detection of video highlights. Highlights can be detected on the basis of players’ or referees’ gestures and postures. Some gestures and postures of players are very typical for special sports events. These special gestures and postures can be recognized mainly in close-up and medium close view shots. The effective view classification method should be first applied. In the paper sports video shots favorable to detect gesture and posture of players are characterized and then experimental results of the tests with video shot categorization based on gesture recognition are presented. Then important and interesting moments in soccer games are detected when referees hold the penalty card above the head and look towards the player that has committed a serious offense. This recognition process is based only on visual information of sports videos and does not use any sensors.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Kazimierz Choroś

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  1. Kazimierz Choroś
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Correspondence to Kazimierz Choroś .

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Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Choroś, K. (2017). Highlights Extraction in Sports Videos Based on Automatic Posture and Gesture Recognition. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_58

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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