https://github.com/wmcnally/golfdb

GolfDB is a video database for Golf Swing Sequencing, which involves detecting 8 golf swing events in trimmed golf swing videos. This repo demos the baseline model, SwingNet.
https://github.com/wmcnally/golfdb

action-recognition event-detection golf pytorch temporal video

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GolfDB is a video database for Golf Swing Sequencing, which involves detecting 8 golf swing events in trimmed golf swing videos. This repo demos the baseline model, SwingNet.

• Host: GitHub
• URL: https://github.com/wmcnally/golfdb
• Owner: wmcnally
• Created: 2019-03-11T17:30:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-01-24T05:39:21.000Z (over 1 year ago)
• Last Synced: 2024-07-13T02:31:53.422Z (2 months ago)
• Topics: action-recognition, event-detection, golf, pytorch, temporal, video
• Language: Python
• Homepage:
• Size: 646 KB
• Stars: 192
• Watchers: 11
• Forks: 59
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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• jimsghstars - wmcnally/golfdb - GolfDB is a video database for Golf Swing Sequencing, which involves detecting 8 golf swing events in trimmed golf swing videos. This repo demos the baseline model, SwingNet. (Python)

README

        
# GolfDB: A Video Database for Golf Swing Sequencing

The code in this repository is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/). 

## Introduction

GolfDB is a high-quality video dataset created for general recognition applications 

in the sport of golf, and specifically for the task of golf swing sequencing. 

This repo contains a simple PyTorch implemention of the SwingNet baseline model presented in the 

[paper](https://arxiv.org/abs/1903.06528).

The model was trained on split 1 **without any data augmentation** and achieved an average PCE of 71.5% (PCE

of 76.1% reported in the paper is credited to data augmentation including horizontal flipping and affine 

transformations). 

If you use this repo please cite the GolfDB paper:

```

@InProceedings{McNally_2019_CVPR_Workshops,

author = {McNally, William and Vats, Kanav and Pinto, Tyler and Dulhanty, Chris and McPhee, John and Wong, Alexander},

title = {GolfDB: A Video Database for Golf Swing Sequencing},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

month = {June},

year = {2019}

}

```

## Dependencies

* [PyTorch](https://pytorch.org/)

## Getting Started

Run [generate_splits.py](./data/generate_splits.py) to convert the .mat dataset file to a dataframe and 

generate the 4 splits.

### Train

* I have provided the preprocessed video clips for a frame size of 160x160 (download 

[here](https://drive.google.com/file/d/1uBwRxFxW04EqG87VCoX3l6vXeV5T5JYJ/view?usp=sharing)). 

Place 'videos_160' in the [data](./data/) directory. 

If you wish to use a different input configuration you must download the YouTube videos (URLs provided in 

dataset) and preprocess the videos yourself. I have provided [preprocess_videos.py](./data/preprocess_videos.py) to

help with that.

* Download the MobileNetV2 pretrained weights from this [repository](https://github.com/tonylins/pytorch-mobilenet-v2) 

and place 'mobilenet_v2.pth.tar' in the root directory. 

* Run [train.py](train.py)

### Evaluate

* Train your own model by following the steps above or download the pre-trained weights 

[here](https://drive.google.com/file/d/1MBIDwHSM8OKRbxS8YfyRLnUBAdt0nupW/view?usp=sharing). Create a 'models' directory

if not already created and place 'swingnet_1800.pth.tar' in this directory.

* Run [eval.py](eval.py). If using the pre-trained weights provided, the PCE should be 0.715.  

### Test your own video

* Follow steps above to download pre-trained weights. Then in the terminal: `python3 test_video.py -p test_video.mp4`

* **Note:** This code requires the sample video to be cropped and cut to bound a single golf swing. 

I used online video [cropping](https://ezgif.com/crop-video) and [cutting](https://online-video-cutter.com/) 

tools for my golf swing video. See test_video.mp4 for reference.

Good luck!