Screen-Strategy Analysis in Broadcast Basketball Video using Player Tracking
Tsung-Sheng Fu , Hua-Tsung Chen , Chien-Li Chou , Wen-Jiin Tsai , and Suh-Yin LeeVisual Communications and Image Processing (VCIP), 2011 IEEE, 6-9 Nov. 2011
Outline Introduction System overview Camera calibration Player extraction and tracking Screen-strategy analysis Experimental results Conclusions
Introduction 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]
Basketball: one of the hottest sportso 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.
To design a system capable of telling the executed tactics explicitly
Introduction Scoring : the most important event, complicated task
o Offensive tacticso Break the defenseo Find open chance to shoot
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 tactico Camera calibration o Player tracking
System overview video pre-processing:
o Gathers reusable informationo Accelerates the computation
Content analysis:o Obtain their trajectories
Camera calibration Geometric mapping between world coordinates and image
coordinates.o Heavy loado 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.
Initial Calibration Color filtering: detect white pixels Compute structure matrix within the pixel neighborhood:
Structure can be classified by evaluating the magnitude of the two eigenvalues. • 1 >> 2 : linear structure
(b : Texture region width)
Initial Calibration Hough transform
Initial Calibration Construct an accumulator matrix
Extract the longest horizontal and vertical lines by extracting the local maxima in the accumulator matrix
vote
Initial Calibration Camera parameters : homography matrix H.
Court Model Tracking
Time consuming
Court Model Tracking Predicting the camera parameters for frame t + 1 based on
the previously computed parameters for frames t - 1 and t.
Player Detectionbackground subtraction
Computing the dominant colorwithin the court region
k-means clustering
Player Tracking 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
Screen-strategy analysis
Screen-strategy analysis Screen Detection
o Two offensive players close to each othero At least one defender between the two offensive players standing
close to each other 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
Experimental results 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 clipso Randomly select 10 clips as our training data o Remaining 20 clips are testing data.
Experimental results
Conclusions 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-timeo Professional coaches and players can analyze the
offense tendency of the opposing team efficiently.