1. Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling Jungong Han Dirk Farin Peter H. N. IEEE CSVT 2008

2. Introduction In consumer videos, sports video attracts a large audience Pixel/object-level analysis Extract highlights Event-based system Construct a general framework

3. System Architecture

4. Camera Calibration Introduction

5. Computing the Ground-Plane Homography 1. Line-Pixel Detection Detect white pixels Use additional constraint to prevent large area from being extracted Structure-tensor based filter

6. Computing the Ground-Plane Homography 2. Line-Parameter Estimation Use RANSAC-like algorithm to detect dominant lines Refined by a least-squares approximation Line g

7. Computing the Ground-Plane Homography 3. Court Model Fitting Determine correspondences between the 4 detected lines and the lines in court model Compute the model matching error E through every configuration

8. Computing the Ground-Plane Homography 4. Model Tracking Assume the change in camera speed is small Refine the camera calibration parameters

9. Playing Frame Detection Define a frame with a court as a playing-frame Count the number of white pixels in current fram e Switch to court-detection This is not a playing frame

10. Moving Player Segmentation 1. Build a background model Use 3 Gaussian to model the RGB color space Compute Mahalanobis distance 2. EM-based background subtraction

11. Moving Player Segmentation 3. Player body bounding Detect the foot position The bounding box is compute from the player’s real height

12. Occlusion Handling The occlusion has two properties Obtain the contour of players in binary map Find the peak Use Gaussian distribution to represent the contour

13. Player Tracking Determine the correspondences between one known player in the previous frame and one blob in the current frame Adopt the DES operator to smooth and refine the motion of each player [23]

14. Scene Level Feature factor Event classification Service in single game Both-net in a double game

15. Experiment Results

16. Precision=98.04% Recall=94.39%

17. Experiment Results

18. The performance of player position refinement

19. Experiment Results serviceBaseline rally Net approach

20. System Efficiency

21. Conclusion The new algorithm shows a detection rate/accuracy of 90-98% At the scene level, the system was able to classify some simple events.