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Game Event Classification in Ice Hockey Game Film

Game Event Classification in Ice Hockey Game Film. Phil Cohn Advised by Aaron Cass Capstone Project Fall 2012. Video Analysis. Coaching staff wants to be able to find and evaluate game events quickly and efficiently

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Game Event Classification in Ice Hockey Game Film

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  1. Game Event Classificationin Ice Hockey Game Film Phil Cohn Advised by Aaron Cass Capstone Project Fall 2012

  2. Video Analysis • Coaching staff wants to be able to find and evaluate game events quickly and efficiently • Our job is to tag segments of game film that are helpful for coaching purposes • Examples include: scoring chances, breakouts, turnovers defensive schemes, face-offs, and more…

  3. Existing Software • Completely manual annotation

  4. Key Questions • Can we identify events during the game automatically? • What attributes of game-play will tell us meaningful information about what is happening on the ice? • Are certain events better fit for automatic identification than others and why?

  5. Related Work • Object tracking (Choi 2010, Pirsiavash 2011, Yilmaz 2006) • Field extraction (Ridder 1995) • Landmark detection (Wang 2004) • Uniform detection (Lu 2009) • Action Recognition (Li 2007)

  6. Our Task • Use attribute information about each player that is accessible using these techniques, to classify game events in game film

  7. Game Events • Face-offs • Breakouts • Scoring Chances

  8. Face-off

  9. Breakout D-Zone N-Zone O-Zone

  10. Scoring Chance

  11. Attributes • Location Attributes F : O – S : F F : O – S : B • Orientation Attributes

  12. Player Location • Zone location • Grid location

  13. Player Orientation • Is the player facing his offensive or defensive zone? • Is the player skating forward or backwards? 1 2 5 3 4

  14. Experimental Data • 40 minutes of footage from 2 games • Extract one frame every 5 seconds of footage • Generate this data manually

  15. Process We manually identify each player attribute and the game event for each frame. Breakout Face-off Scoring Chance

  16. Process

  17. Game Event Classification

  18. Location Granularity

  19. Attribute Manipulation

  20. Attribute Manipulation

  21. Future Work • Investigate classification results with more realistic attribute accuracy • Simulations vs. automatic data gathering from video • Investigate other game events • How accurate could our system be for other sports? • Is our system applicable for other purposes?

  22. Questions?

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