Two Stream Network for Stroke Detection in Table Tennis
Anam Zahra, Pierre-Etienne Martin
Abstract
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.
Results
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Action Detection | TTStroke-21 ME21 | IoU | 0.07 | Two Stream Network |
| Action Detection | TTStroke-21 ME21 | mAP | 0.00124 | Two Stream Network |
Related Papers
CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment2025-06-25MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans2025-06-25Distributed Activity Detection for Cell-Free Hybrid Near-Far Field Communications2025-06-17Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm2025-06-03Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion2025-06-02Joint Activity Detection and Channel Estimation for Massive Connectivity: Where Message Passing Meets Score-Based Generative Priors2025-05-31Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM2025-05-29Robust Activity Detection for Massive Random Access2025-05-21