Automatic DTW-Based Grading for Golf Swing in Sports

how to run

# follow VideoPose3d PoseFormer to download some model weights
# install requirements
pip install -r requirements.txt

# set your path
vim project_config.py

# for GUI
python -m data.gui.gui

# for 2d points visualization
# example (use PE4.mp4)
python -m data.tool.draw_chart data/3d_point_vis/PE4.mp4.npy PE4.mp4

# for analysis see the script folder('data/shell script')

DHU-Golf and data

video -> data/video_raw
grades -> data/grades
hit events label -> data/tag
posture data -> data/3d_point* data/2d_point

model weights

# CBAM-SwingNet
data/golfdb/models/split_4_flip+affine_7700.pth.tar
# others to follow VideoPose3d, PoseFormer and SwingNet

results

PCE of CBAM-SwingNet in golfdb evaluate

model	PCE
SwingNet	76.1% (reported in the paper)
CBAM-SwingNet	80.5% (split_4_flip+affine_7700.pth.tar)

F1-score for start-end detect (1~396 remove defective video)

Methods	F1-score-aver
SwingNet-based	0.7425
distance_threshold-based	0.7599
CBAM-SwingNet-based	0.779

Pearson Correlation Coefficient (distance_threshold + dtw based)

No.	mean	var	pearson	p-value
1~190	0.497646	0.008536	-0.21548	0.0028
1~396	0.531162	0.011472	-0.15446	0.0021

For details, see the csv and txt files in the results.

Acknowledgement

VideoPose3d

PoseFormer

golfdb

dtw-python