Sports Ball Detection And Tracking On
Metrics
Accuracy (%)
Average Precision (%)
F1 (%)
Results
Performance results of various models on this benchmark
Model Name | Accuracy (%) | Average Precision (%) | F1 (%) | Paper Title | Repository |
|---|---|---|---|---|---|
| ResTrackNetV2 | 84.0 | 82.2 | 89.4 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | - |
| WASB (Step=3) | 87.0 | 88.5 | 91.6 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | - |
| DeepBall | 38.6 | 60.0 | 52.4 | DeepBall: Deep Neural-Network Ball Detector | - |
| DeepBall-Large | 36.8 | 59.5 | 50.6 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | - |
| TrackNetV2 | 85.6 | 83.6 | 90.5 | TrackNetV2: Efficient Shuttlecock Tracking Network | |
| BallSeg | 72.2 | 68.4 | 79.9 | Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup | - |
| WASB (Step=1) | 89.0 | 91.6 | 93.1 | Widely Applicable Strong Baseline for Sports Ball Detection and Tracking | - |
| MonoTrack | 85.9 | 84.9 | 90.9 | MonoTrack: Shuttle trajectory reconstruction from monocular badminton video | - |
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