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Tennis Player and Ball

TrackingYeshwanth Malekar

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CONTENT

MotivationChallenges

Previous Work

Algorithm

DetectionSolution

Problems

Results Future Work

References

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Motivation

Tracking Tennis players and ball in a Tennis ma

Useful for automatic tennis video annotation.

Player and ball trajectories contain high levelinformation which can be used for training pudesigning strategies.

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Project Goal

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Challenges

Video taken is not from a steady camera.

The speed and size of the ball pose real probl

Most of the times, ball undergoes either occlublurred in the background.

Chances of false detection are high.

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Previous Work

Most of the previous work is done on a video ta steady camera.

They also used Kalman filter and Particle filtersthe location of foreground objects.

These filters need a steady camera to work onmodel of the movement of players and balls.

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Algorithm

1. Extract Background image.

2. Frame difference method.

3. Morphological Operations.

4. Blob analysis.

5. Detect players and ball.

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Background extraction using median function.

Bad background examples:

Too few Frames: Over all frames:

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Foreground Separation Morphological Operation

Player and Ball detection

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Conditions

Added extra boundary conditions for the ball

Finally centroid conditions to keep track of theand ball in the following frames.

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What if camera moves?

The whole court is detected in thedifference image instead of justForeground.

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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 imaprevious image.

We have new approximated candidate positions for the blan

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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 dete

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Advantages (Cont.)

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Problems encountered

The ball moves at high speeds and changes itrapidly.

If detection in a frame is missed, keeping trackball in the next frame with this approximation not possible.

False detections influence further false detect

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Problems (Cont.)

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Results

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Results (Cont.)

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Result (Doubles)

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Future Work

Better ball detection techniques for a moving

Would also like to work on different ball gamesoccer.

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References

Hira Fatima et al, “Object Recognition, Tracking and Trajectoin Real-Time Video Sequence”, International Journal of InformElectronics Engineering vol.3, no.6, pp. 639-642, 2013.

Amor Salorpour et al, “Vehicle Tracking Using Kalman Filter anSignal & Image Processing, an International Journal (SIPIJ), VJune 2011.

F. Yan and W. Christmas and J. Kittler, “Tennis Ball Tracking AlgAutomatic Annotation of Tennis Match”, BMVC, pp 619-628, 2

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Questions