Tennis Player and Ball

Tracking Yeshwanth Malekar

CONTENT

Motivation

Challenges

Previous Work

Algorithm

Detection

Solution

Problems

Results

Future Work

References

Motivation

Tracking Tennis players and ball in a Tennis match

Useful for automatic tennis video annotation.

Player and ball trajectories contain high level

information which can be used for training purposes and

designing strategies.

Project Goal

Challenges

Video taken is not from a steady camera.

The speed and size of the ball pose real problems.

Most of the times, ball undergoes either occlusion or gets

blurred in the background.

Chances of false detection are high.

Previous Work

Most of the previous work is done on a video taken from

a steady camera.

They also used Kalman filter and Particle filters to predict

the location of foreground objects.

These filters need a steady camera to work on and a

model of the movement of players and balls.

Algorithm

1. Extract Background image.

2. Frame difference method.

3. Morphological Operations.

4. Blob analysis.

5. Detect players and ball.

Background extraction using median function.

Bad background examples:

Too few Frames: Over all frames:

Foreground Separation Morphological Operations

Player and Ball detection

Conditions

Added extra boundary conditions for the ball.

Finally centroid conditions to keep track of the players

and ball in the following frames.

What if camera moves?

The whole court is detected in the

difference image instead of just

Foreground.

Simple solution

Skipped through the frames with more than required blobs.

Continue detecting the players when the video is stabilized.

Saved the centroid of the players detected in all the frames.

Adjustments:

Added the difference of the centroid positions of last two images to the

previous image.

We have new approximated candidate positions for the blank frame.

Advantages of this solution

It is very simple technique to implement.

Players detection is excellent.

Even if the ball is occluded or blurred, it is detected.

Advantages (Cont.)

Problems encountered

The ball moves at high speeds and changes its direction

rapidly.

If detection in a frame is missed, keeping track of the

ball in the next frame with this approximation method is

not possible.

False detections influence further false detections.

Problems (Cont.)

Results

Results (Cont.)

Result (Doubles)

Future Work

Better ball detection techniques for a moving camera.

Would also like to work on different ball games such as

soccer.

References

Hira Fatima et al, “Object Recognition, Tracking and Trajectory Generation

in Real-Time Video Sequence”, International Journal of Information and

Electronics Engineering vol.3, no.6, pp. 639-642, 2013.

Amor Salorpour et al, “Vehicle Tracking Using Kalman Filter and Features”,

Signal & Image Processing, an International Journal (SIPIJ), Vol.2, No.2,

June 2011.

F. Yan and W. Christmas and J. Kittler, “Tennis Ball Tracking Algorithm for

Automatic Annotation of Tennis Match”, BMVC, pp 619-628, 2005.

Questions