Object Tracking on Tennis

Metric: Accuracy (%) (higher is better)

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#	Model↕	Accuracy (%)▼	Extra Data	Paper	Date↕	Code
1	WASB (Step=1)	91.8	No	Widely Applicable Strong Baseline for Sports Bal...	2023-11-09	Code
2	WASB (Step=3)	89	No	Widely Applicable Strong Baseline for Sports Bal...	2023-11-09	Code
3	MonoTrack	85.9	No	MonoTrack: Shuttle trajectory reconstruction fro...	2022-04-04	Code
4	ResTrackNetV2	82.8	No	Widely Applicable Strong Baseline for Sports Bal...	2023-11-09	Code
5	TrackNetV2	81.4	No	-	-	Code
6	BallSeg	57.5	No	Real-time CNN-based Segmentation Architecture fo...	2020-07-23	Code
7	DeepBall	32.3	No	DeepBall: Deep Neural-Network Ball Detector	2019-02-19	Code
8	DeepBall-Large	31.6	No	Widely Applicable Strong Baseline for Sports Bal...	2023-11-09	Code

#1WASB (Step=1)SOTA
91.8
Accuracy (%)· 2023-11-09
Widely Applicable Strong Baseline for Sports Ball Detection and Tracking Code
#2WASB (Step=3)
89
Accuracy (%)· 2023-11-09
Widely Applicable Strong Baseline for Sports Ball Detection and Tracking Code
#3MonoTrackSOTA
85.9
Accuracy (%)· 2022-04-04
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video Code
#4ResTrackNetV2
82.8
Accuracy (%)· 2023-11-09
Widely Applicable Strong Baseline for Sports Ball Detection and Tracking Code
#5TrackNetV2
81.4
Accuracy (%)
No paperCode
#6BallSegSOTA
57.5
Accuracy (%)· 2020-07-23
Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup Code
#7DeepBallSOTA
32.3
Accuracy (%)· 2019-02-19
DeepBall: Deep Neural-Network Ball Detector Code
#8DeepBall-Large
31.6
Accuracy (%)· 2023-11-09
Widely Applicable Strong Baseline for Sports Ball Detection and Tracking Code