Project: Sports Video Analysis

Current Results

Play-Break Segmentation (For the current results, see the project page on DVMM)
According to soccer syntax and production rules mentioned in last section, play-break segmentation is done by mapping grass area to view, and view sequence to game status.

Three steps of the algorithm:
1. Grass value learning:
- Automatically tuned to the grass value of the current clip
- Periodically updated to suit gradual change of the field condition
- Consistent and accurate result
2. View classification
- Classifying grass area with automatically adjusted thresholds
- Best accuracy: 92%
- Most of the errors due to model breakdown
3. Play-break segmentation
- View label sequence processing using heuristic rules
- Different kinds of evaluation metric for different application
- Best global accuracy: 86.5%
- Boundary timing to be improved
This result can be used for further inference, such as grass angle classification (best accuracy of 88.5% achieved), or combined with complementary features such as motion or field audio to improve segmentation or do event detection.
P. Xu, L. Xie, S.-F. Chang, A. Divakaran, A. Vetro, H. Sun, "Algorithms and Systems for Segmentation and Structure Analysis in Soccer Video", IEEE International Conference on Multimedia and Expo, Tokyo, Japan, Aug. 22-25, 2001 (PDF )

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Last update: 06/15/2001