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

System Architecture

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]

Scene Level

Feature factor

Event classificationService in single gameBoth-net in a double game

Experiment Results

Test sequences are recorded from TV broadcasts4 tennis, 3 badminton, and 2 volleyball gamesResolutions ： and Robustness

Experiment Results

Precision=98.04%Recall=94.39%

Experiment Results

Experiment Results

The performance of player position refinement

Experiment Results

service Baseline rally Net approach

System Efficiency

The efficiency depends on image resolution and content complexity

Eg. 473.8 ms per frame

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.