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Tsung-Sheng Fu , Hua-Tsung Chen , Chien-Li Chou , Wen-Jiin Tsai , and Suh-Yin Lee
Visual Communications and Image Processing (VCIP), 2011 IEEE, 6-9 Nov. 2011
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Introduction
System overview
Camera calibration
Player extraction and tracking
Screen-strategy analysis
Experimental results
Conclusions
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Sports video analysis
o Bring the audience efficient viewing of sports games
• Highlight extraction and semantic event analysis [1, 2, 3].
o systems for tactics analysis and statistics compiling are in urgent
demand [4, 5, 6]
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Basketball: one of the hottest sports
o Chen et al. [7] proposed a 3D ball trajectory reconstruction algorithm
which can be applied to shooting location estimation.
o Chang et al. [8] introduced a wide-open warning system.
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To design a system capable of telling the executed tactics
explicitly
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Scoring : the most important event, complicated task
o Offensive tactics
o Break the defense
o Find open chance to shoot
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With the tactic information, audience can learn how plays are
made, and professional coaches and players can analyze the
offense tendencies and strategies.
Screen：basic offensive tactic
o Camera calibration
o Player tracking
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video pre-processing:
o Gathers reusable information
o Accelerates the computation
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Content analysis:
o Obtain their trajectories
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Geometric mapping between world coordinates and image
coordinates.
o Heavy load
o Adapt the efficient court model tracking algorithm in [9]
[9] D. Farin, S. Krabbe, P. H. N. de With, W. Effelsberg, “Robust Camera Calibration for Sport
Videos Using Court Models,” in Proc. SPIE, pp. 80-91, 2004.
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Color filtering：detect white pixels
Compute structure matrix within the
pixel neighborhood:
(b : Texture region width)
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Structure can be classified by evaluating
the magnitude of the two eigenvalues.
• 1 >> 2 : linear structure
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Hough transform
Extract the longest horizontal and vertical lines by
extracting the local maxima in the accumulator matrix
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Construct an accumulator matrix
vote
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Camera parameters : homography matrix H.
Time
consuming
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Predicting the camera parameters for frame t + 1 based on
the previously computed parameters for frames t - 1 and t.
Computing the dominant color
within the court region
background subtraction
k-means clustering
Kalman filter
o With the position predicted by the Kalman
filter, we select the nearest candidate as
measurement.
o If a tracker is outside the court for
consecutive n frames, it will be terminated
o there are some candidates not tracked
=>add new trackers
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Screen Detection
o Two offensive players close to each other
o At least one defender between the two offensive players standing
close to each other
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Screen Classification
o down-screen: screener moves to the baseline.
o back-screen: the angle between the two directions is small, otherwise,
mark as front-screen
• moving direction of the screenee
• the direction to the basket of the screenee
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Testing videos: Beijing 2008 Olympic Games: USA vs. AUS,
ARG vs. USA, and USA vs.CHN with frame resolution of
640x352 (29.97 fps).
total of 30 video clips
o Randomly select 10 clips as our training data
o Remaining 20 clips are testing data.
 We proposed
a system that detects and
classifies screens in basketball video
 Our proposed system is a one-pass scheme so
that it can be applied to broadcast video.
o The audience can learn offensive basketball tactics
in real-time
o Professional coaches and players can analyze the
offense tendency of the opposing team efficiently.