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Extracting Highlights from a Badminton Video Combine Transfer Learning with Players’ Velocity

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Computer Animation and Social Agents (CASA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1300))

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Abstract

We present a novel method for extracting highlights from a badminton video. Firstly, we classify the different views of badminton videos for video segmentation through building classification model based on transfer learning, and achieve high-precision with real-time segmentation. Secondly, based on object detection by the object detecting model YOLOv3, we locate players in a video segment and calculate the players’ average velocity to extract highlights from a badminton video. Video segments with higher players’ average velocity reflect the intense scenes of a badminton game, so we can regard them as highlights in a way. We extract highlights by sorting badminton video segments with higher players’ average velocity, which make users save their time to enjoy the highlights of an entire video. We laterally evaluate the proposed method through verifying whether a segment has admitted objective details such as exciting response from audiences and positive evaluation from narrators.

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Notes

  1. 1.

    https://github.com/taoshu1996/BUILDINGTAO.

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Acknowledgement

This work is partially funded by Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, China (2018AIOT-09), Key Research and Development Program of Shaanxi Province (2018NY-127), and supported by the Shaanxi Key Industrial Innovation Chain Project in Agricultural Domain (Grant No. 2019ZDLNY02-05).

Author information

Authors and Affiliations

  1. College of Information Engineering, Northwest A & F University, Xianyang, China

    Shu Tao, Jiankun Luo, Jing Shang & Meili Wang

  2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, China

    Meili Wang

  3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, China

    Meili Wang

Authors
  1. Shu Tao
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  2. Jiankun Luo
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  3. Jing Shang
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  4. Meili Wang
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Corresponding author

Correspondence to Meili Wang .

Editor information

Editors and Affiliations

  1. Faculty of Science and Technology, Bournemouth University, Poole, UK

    Feng Tian

  2. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Xiaosong Yang

  3. École Polytechnique Fédérale de Lausa, Lausanne, Switzerland

    Daniel Thalmann

  4. Computer Science Department, Zhejiang University, Hangzhou, China

    Weiwei Xu

  5. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Jian Jun Zhang

  6. MIRALab/C.U.I., University of Geneva, Geneva, Switzerland

    Nadia Magnenat Thalmann

  7. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Jian Chang

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Tao, S., Luo, J., Shang, J., Wang, M. (2020). Extracting Highlights from a Badminton Video Combine Transfer Learning with Players’ Velocity. In: Tian, F., et al. Computer Animation and Social Agents. CASA 2020. Communications in Computer and Information Science, vol 1300. Springer, Cham. https://doi.org/10.1007/978-3-030-63426-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-63426-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63425-4

  • Online ISBN: 978-3-030-63426-1

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