Game Event Classification

in Ice Hockey Game Film

Phil CohnAdvised by Aaron Cass

Capstone ProjectFall 2012

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…

Existing Software Completely manual annotation

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?

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)

Our Task Use attribute information about each player

that is accessible using these techniques, to classify game events in game film

Game Events Face-offs Breakouts Scoring Chances

Face-off

Breakout

D-Zone O-ZoneN-Zone

Scoring Chance

Attributes

Location Attributes Orientation Attributes

F : O – S : F F : O –

S : B

Player Location

Zone location

Grid location

Player Orientation Is the player facing his offensive or

defensive zone? Is the player skating forward or backwards?

12

54 3

Pl# F S1 O F2 O F3 O F4 D F5 D F

Experimental Data

40 minutes of footage from 2 games Extract one frame every 5 seconds of

footage Generate this data manually

Process

We manually identify each player attribute and the game event for each frame.

H1_Z

H1_G

H1_F

H1_S

H2_Z

H2_G

H2_F

H2_S

… GME

N 11 O F N 11 O F … FO

ProcessH1_Z

H1_G

H1_F

H1_S

H2_Z

H2_G

H2_F

H2_S

…

N 11 O F N 11 O F …

Face-off Breakout Scoring Chance

Unknown

Game Event Classification

Class J48 Ibk(5) Naïve Bayes

All Events 83.3% 83.5% 71.9%Breakout 90.6% 90.2% 85.8%

Scoring Chance 95.0% 94.4% 87.1%Face-off 96.5% 96.9% 95.2%

Location Granularity

Class Zones Grid BothAll Events 82.1% 83.3% 83.3%Breakout 90.4% 90.4% 90.6%Scoring Chance 95.0% 95.0% 95.0%Face-off 96.5% 96.7% 96.5%

Attribute Manipulation

Attribute Abbr. Accuracy

Zone Z 82.3%Grid G 81.3%

Facing F 81.7%Skating S 81.3%

Attribute Manipulation

# of Attributes Attribute Subset Accuracy

4 (Z - G - F - S) 83.3%3 (G - F - S) 83.3%2 (G - F) 84.2%1 (G) 81.3%

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?

Questions?