broadcast court-net sports video analysis using fast 3-d camera modeling jungong han dirk farin...
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Broadcast Court-Net Sports Video Analysis
Using Fast 3-D Camera Modeling
Jungong Han
Dirk Farin
Peter H. N.
IEEE CSVT 2008
Introduction
In consumer videos, sports video attracts a large audience
Pixel/object-level analysisExtract highlightsEvent-based systemConstruct a general framework
Camera Calibration Introduction
Map the points in real world coordinates to the image domain
Assume the ground plane is placed at , so the homography-matrix H is:
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
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
𝝉
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
:the closest line segment in image
Computing the Ground-Plane Homography
4. Model Tracking Assume the change in camera speed is small
Refine the camera calibration parameters
Playing Frame Detection
Define a frame with a court as a playing-frame
Count the number of white pixels in current frame
Switch to court-detection
This is not a playing frame
If
If
Moving Player Segmentation
1. Build a background modelUse 3 Gaussian to model the RGB color spaceCompute Mahalanobis distance
2. EM-based background subtraction
Moving Player Segmentation
3. Player body bounding Detect the foot position The bounding box is compute
from the player’s real height
Occlusion Handling
The occlusion has two propertiesObtain the contour of players in binary mapFind the peakUse Gaussian distribution to represent the contour
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]
Experiment Results
Test sequences are recorded from TV broadcasts4 tennis, 3 badminton, and 2 volleyball gamesResolutions : and Robustness
System Efficiency
The efficiency depends on image resolution and content complexity
Eg. 473.8 ms per frame