Sports Ball Detection And Tracking On Sbdt
Metrics
Accuracy (% )
Average Precision (%)
F1 (%)
Results
Performance results of various models on this benchmark
Model Name | Accuracy (% ) | Average Precision (%) | F1 (%) | Paper Title | Repository |
|---|---|---|---|---|---|
| WASB (Step=1) | 97.9 | 86.2 | 88.2 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | |
| MonoTrack | 97.4 | 78.6 | 85.2 | MonoTrack: Shuttle trajectory reconstruction from monocular badminton video | |
| WASB (Step=3) | 97.9 | 83.6 | 88.3 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | |
| ResTrackNetV2 | 97.4 | 75.5 | 84.6 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | |
| BallSeg | 92.6 | 20.0 | 36.1 | Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup | |
| DeepBall-Large | 89.5 | 34.0 | 44.9 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | |
| TrackNetV2 | 97.7 | 77.2 | 86.6 | TrackNetV2: Efficient Shuttlecock Tracking Network | |
| DeepBall | 92.7 | 26.3 | 44.5 | DeepBall: Deep Neural-Network Ball Detector |
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