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Improved Method of Detecting Replay Logo in Sports Videos Based on Contrast Feature and Histogram Difference

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9875))

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Abstract

Sports videos are probably the most popular videos and the most frequently searched in the Web. Similarly to text documents for which abstracts can be automatically generated we need to automatically summarize the long videos presenting for example the whole soccer matches. The obvious solution to summarize a sports video is to detect and to extract replay segments as highlights which usually present the most interesting and exciting parts of a video containing important players and actions. Replays can be detected if replay shots are separated by special logo animations or replays are in slow motion. One of the automatic method of logo transition detection is a method based on contrast feature and histogram difference. The paper presents the improved method of replay logo detection and the results of the tests demonstrating the benefits of the proposed improvements.

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Authors and Affiliations

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

    Kazimierz Choroś & Adam Gogol

Authors
  1. Kazimierz Choroś
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  2. Adam Gogol
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Corresponding author

Correspondence to Kazimierz Choroś .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Management, Democritus University of Thrace, Orestiada, Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

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Choroś, K., Gogol, A. (2016). Improved Method of Detecting Replay Logo in Sports Videos Based on Contrast Feature and Histogram Difference. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_50

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  • DOI: https://doi.org/10.1007/978-3-319-45243-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45242-5

  • Online ISBN: 978-3-319-45243-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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