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Replay and key-events detection for sports video summarization using confined elliptical local ternary patterns and extreme learning machine

  • Ali Javed
  • Aun Irtaza
  • Yasmeen Khaliq
  • Hafiz Malik
  • Muhammad Tariq MahmoodEmail author
Article

Abstract

Sports broadcasters generate enormous amount of video content viewed all over the world. To capture the user interests in the rebroadcasted content, the sports videos are summarized that need the manual inspection and analysis. However, the huge repository and long duration of videos make manual analysis and summarization a laborious and time-consuming job. To overcome this problem, efforts have been made for automatic video summarization. In this paper, a novel framework to summarize sports videos is presented. It has been observed that the replays within a sports video represent key-events and these events can be used for video summarization. It has been noted that replays are usually sandwiched between start and stop of gradual-transitions. A thresholding-based approach is used to identify gradual transition effect (i.e. fade-in, fade-out) in sports video. The Gaussian mixture model (GMM) is then applied to key-event candidates to extract silhouettes and generate motion history image (MHI) for each key-event. The MHIs are processed using Confined Elliptical Local Ternary Patterns (CE-LTPs) for feature extraction. Extreme learning machine (ELM) classifier is used to learn the underlying model for events. A trained ELM-based classifier is then used for key-event detection. The output of classifier is then used for key-event labeling, replay detection, and complete game summarization. Performance of the proposed framework is evaluated on a dataset consisting of 20 videos of four different sports. Experimental results indicate the effectiveness of the proposed framework in terms of replays and key-events detection from selected dataset.

Keywords

Gradual transition Highlights Replay detection Events detection 

Notes

Acknowledgments

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03933860).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Software Engineering DepartmentUniversity of Engineering and TechnologyTaxilaPakistan
  2. 2.Computer Science DepartmentUniversity of Engineering and TechnologyTaxilaPakistan
  3. 3.Computer Science DepartmentCOMSATS University Islamabad, Wah CampusWah CanttPakistan
  4. 4.College of Engineering and Computer ScienceUniversity of Michgan-DearbornDearbornUSA
  5. 5.School of Computer Science and EngineeringKorea University of Technology and EducationCheonanSouth Korea

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