Papers › Two Stream Network for Stroke Detection in Table Tennis
Two Stream Network for Stroke Detection in Table Tennis
Anam Zahra, Pierre-Etienne Martin
This paper presents a table tennis stroke detection method from videos. The method relies on a two-stream Convolutional Neural Network processing in parallel the RGB Stream and its computed optical flow. The method has been developed as part of the MediaEval 2021 benchmark for the Sport task. Our contribution did not outperform the provided baseline on the test set but has performed the best among the other participants with regard to the mAP metric.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Detection | TTStroke-21 ME21 | Two Stream Network | IoU | 0.070 | #2 of 2 | Archive leaderboard | report |
| Action Detection | TTStroke-21 ME21 | Two Stream Network | mAP | 0.00124 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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