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Event Detection in Broadcasting Video for Halfpipe Sports

Authors:

Ja-Ling WuAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 727 - 728

https://doi.org/10.1145/2647868.2654883

Published: 03 November 2014 Publication History

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Abstract

In this work, a low-cost and efficient system is proposed to automatically analyze the halfpipe (HP) sports videos. In addition to the court color ratio information, we find the player region by using salient object detection mechanisms to face the challenge of motion blurred scenes in HP videos. Besides, a novel and efficient method for detecting the spin event is proposed on the basis of native motion vectors existing in a compressed video. Experimental results show that the proposed system is effective in recognizing the hard-to-be-detected spin events in HP videos.

References

[1]

J. Harding, C. Mackintosh, A. Hahn, and D. James. Classification of aerial acrobatics in elite half-pipe snowboarding using body mounted inertial sensors. In The Eng. of Sport 7, pages 447--456. Springer Paris, 2008.

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[2]

T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. IEEE trans. PAMI, 33(2), 2011.

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[3]

J. Ning, L. Zhang, D. Zhang, and C. Wu. Robust mean-shift tracking with corrected background-weighted histogram. IET Comput. Vis., 6(1):62--69, January 2012.

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Cited By

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Ntalianis KTsapatsoulis N(2021)Unsupervised sports video particles annotation based on social latent semantic analysis2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7350792(222-226)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/ICIP.2015.7350792
Liu WYan CLiu JMa H(2017)Deep learning based basketball video analysis for intelligent arena applicationMultimedia Tools and Applications10.1007/s11042-017-5002-576:23(24983-25001)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11042-017-5002-5

Index Terms

Event Detection in Broadcasting Video for Halfpipe Sports
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2014

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MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Ntalianis KTsapatsoulis N(2021)Unsupervised sports video particles annotation based on social latent semantic analysis2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7350792(222-226)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/ICIP.2015.7350792
Liu WYan CLiu JMa H(2017)Deep learning based basketball video analysis for intelligent arena applicationMultimedia Tools and Applications10.1007/s11042-017-5002-576:23(24983-25001)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11042-017-5002-5

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Event Detection based on Twitter Enthusiasm Degree for Generating a Sports Highlight Video

Volumetric Features for Video Event Detection

Offense based temporal segmentation for event detection in soccer video

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