Table 3 Compare with methods on the pickleball dataset. In this experiment, we re-implement the SOTA methods on this dataset.
From: Curvelet-enhanced transformer architecture for blurred action fine-grained detection
Methods | Backbone | Frame size | AP | AP75 | APM | APL |
|---|---|---|---|---|---|---|
SwinT | SwinT | 256 × 192 | 0.755 | 0.795 | 0.723 | 0.801 |
SimpleBaseline | ResNet-50 | 256 × 192 | 0.748 | 0.768 | 0.704 | 0.781 |
DERK | HRNet-W32 | 512 × 512 | 0.751 | 0.776 | 0.743 | 0.802 |
HigherHRNet + SWAHR | HRNet-W32 | 512 × 512 | 0.786 | 0.799 | 0.771 | 0.793 |
AECA | ResNet-18 | 384 × 288 | 0.769 | 0.787 | 0.762 | 0.810 |
EBA | ResNet-18 | 256 × 255 | 0.799 | 0.821 | 0.756 | 0.809 |
TokenPose | TokenPose-L/D24 | 256 × 192 | 0.796 | 0.819 | 0.751 | 0.810 |
RIFormer | HRFormer-B | 256 × 192 | 0.801 | 0.818 | 0.764 | 0.813 |
MCTN | DETR | 256 × 192 | 0.813 | 0.837 | 0.775 | 0.822 |
MCTN | RT-DETRv3 | 256 × 192 | 0.822 | 0.841 | 0.778 | 0.846 |
MCTN | DETR | 384 × 288 | 0.816 | 0.837 | 0.777 | 0.827 |
MCTN | RT-DETRv3 | 384 × 288 | 0.822 | 0.846 | 0.780 | 0.844 |