Screen-Strategy Analysis in Broadcast Basketball Video using Player Tracking

1. 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 Lee Visual Communications and Image Processing (VCIP), 2011 IEEE,  6-9 Nov. 2011

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

3. Introduction • Sports video analysis • Bring the audience efficient viewing of sports games • Highlight extraction and semantic event analysis [1, 2, 3]. • systems for tactics analysis and statistics compiling are in urgent demand [4, 5, 6] • Basketball: one of the hottest sports • Chen et al. [7] proposed a 3D ball trajectory reconstruction algorithm which can be applied to shooting location estimation. • Chang et al. [8] introduced a wide-open warning system. • To design a system capable of telling the executed tactics explicitly

4. Introduction • Scoring : the most important event, complicated task • Offensive tactics • Break the defense • 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 • Camera calibration • Player tracking

5. System overview • video pre-processing: • Gathers reusable information • Accelerates the computation • Content analysis: • Obtain theirtrajectories

6. Camera calibration • Geometric mappingbetween worldcoordinates and image coordinates. • Heavy load • Adapt theefficient courtmodel 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. Initial Calibration • Color filtering：detectwhite 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. Initial Calibration • Hough transform

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

11. Initial Calibration • Camera parameters : homographymatrix H.

12. Court Model Tracking Time consuming

13. Court Model Tracking • Predicting the camera parameters for frame t + 1 based on the previously computed parameters for frames t - 1 and t.

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

15. Player Tracking • Kalmanfilter • With the position predicted by the Kalman filter, we select the nearest candidate as measurement. • If a tracker is outside the court for consecutive n frames, it will be terminated • there are some candidates not tracked =>add new trackers

16. Screen-strategy analysis

17. Screen-strategy analysis • Screen Detection • Two offensive players close to each other • At least one defender between the two offensive players standing close to each other • Screen Classification • down-screen: screener moves to the baseline. • 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. Experimental results • Testing videos: Beijing 2008 Olympic Games: USA vs. AUS, ARG vs. USA, and USA vs.CHNwith frame resolution of 640x352 (29.97 fps). • total of 30 video clips • Randomly select 10 clips as our training data • Remaining20 clips are testing data.

19. Experimental results

20. Conclusions • We proposed a system that detects and classifies screens inbasketball video • Our proposedsystem is a one-pass scheme so that it can be applied tobroadcast video. • The audience can learn offensivebasketball tactics in real-time • Professional coachesand players can analyze the offense tendency of the opposingteam efficiently.