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Temporal Segmentation of Basketball Continuous Videos Based on the Analysis of the Camera and Player Movements

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

Sports video segmentation is a very useful process in video indexing, video summarization, or video highlight detections. For continuous videos when the full game is recorded the segmentation cannot be based on the detection of cuts or cross-dissolve transitions as it is the case of edited videos like sports news or other short sports coverages. One basketball game usually lasts even for around two hours including play parts as well as breaks. In the paper the approach of a basketball game segmentation is presented and examined. The proposed method is based on the analysis of the camera and player movements. The tests performed in the AVI Indexer showed that the analysis of the camera and player movements leads to the correct detection of segments with game breaks but also segments with the interesting highlights in continuous basketball videos.

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Department of Applied Informatics, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370, Wrocław, Poland

    Kazimierz Choroś

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  1. Kazimierz Choroś
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Correspondence to Kazimierz Choroś .

Editor information

Editors and Affiliations

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

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

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

    Adrianna Kozierkiewicz

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

    Bogdan Trawiński

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Choroś, K. (2022). Temporal Segmentation of Basketball Continuous Videos Based on the Analysis of the Camera and Player Movements. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_36

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16013-4

  • Online ISBN: 978-3-031-16014-1

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