1.  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

2.  Introduction  System overview  Camera calibration  Player extraction and tracking  Screen-strategy analysis  Experimental results  Conclusions

3.  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 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.  To design a system capable of telling the executed tactics explicitly

4.  Scoring : the most important event, complicated task o Offensive tactics o Break the defense o 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 tactic o Camera calibration o Player tracking

5.  video pre-processing: o Gathers reusable information o Accelerates the computation  Content analysis: o Obtain their trajectories

6.  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.

8.  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)

9.  Hough transform

10.  Construct an accumulator matrix Extract the longest horizontal and vertical lines by extracting the local maxima in the accumulator matrix vote

11.  Camera parameters : homography matrix H.

12. Time consuming

13.  Predicting the camera parameters for frame t + 1 based on the previously computed parameters for frames t - 1 and t.

14. background subtraction Computing the dominant color within the court region k-means clustering

15.  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

17.  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  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

18.  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.

20.  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.