The excitement of sports: Automatic highlights using audio/visual cues

Michele Merler; Dhiraj Joshi; Khoi Nguyen C. Mac; Quoc Bao Nguyen; John Kent; Stephen Hammer; Jinjun Xiong; Minh N. Do; John R. Smith; Rogerio S. Feris

The excitement of sports: Automatic highlights using audio/visual cues

Michele Merler
, Dhiraj Joshi
, Khoi Nguyen C. Mac
, Quoc Bao Nguyen
, John Kent
, Stephen Hammer
, Jinjun Xiong
, Minh N. Do
, John R. Smith
, Rogerio S. Feris

Department of Computer Science and Engineering

University at Buffalo

IBM
University of Illinois at Urbana-Champaign
IBM iX

Research output: Contribution to journal › Conference article › peer-review

10 Scopus citations

Abstract

The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game.

Original language	English
Pages (from-to)	2520-2523
Number of pages	4
Journal	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2018-January
State	Published - 2018
Event	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States Duration: Jun 18 2018 → Jun 22 2018

Cite this

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title = "The excitement of sports: Automatic highlights using audio/visual cues",

abstract = "The production of sports highlight packages summarizing a game{\textquoteright}s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players{\textquoteright} reactions (action recognition such as high-fives and fist pumps), players{\textquoteright} expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game.",

author = "Michele Merler and Dhiraj Joshi and Mac, \{Khoi Nguyen C.\} and Nguyen, \{Quoc Bao\} and John Kent and Stephen Hammer and Jinjun Xiong and Do, \{Minh N.\} and Smith, \{John R.\} and Feris, \{Rogerio S.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE Computer Society. All rights reserved.; 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 ; Conference date: 18-06-2018 Through 22-06-2018",

year = "2018",

language = "English",

volume = "2018-January",

pages = "2520--2523",

journal = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

issn = "2160-7508",

publisher = "IEEE Computer Society",

}

TY - JOUR

T1 - The excitement of sports

T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

AU - Merler, Michele

AU - Joshi, Dhiraj

AU - Mac, Khoi Nguyen C.

AU - Nguyen, Quoc Bao

AU - Kent, John

AU - Hammer, Stephen

AU - Xiong, Jinjun

AU - Do, Minh N.

AU - Smith, John R.

AU - Feris, Rogerio S.

PY - 2018

Y1 - 2018

N2 - The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game.

AB - The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game.

UR - https://www.scopus.com/pages/publications/85067117319

M3 - Conference article

SN - 2160-7508

VL - 2018-January

SP - 2520

EP - 2523

JO - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

JF - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Y2 - 18 June 2018 through 22 June 2018

ER -

The excitement of sports: Automatic highlights using audio/visual cues

Abstract

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