Based on DeepLabCut toolbox, we run with three synthetic datasets
- Easy Pose Datasets: contains T-pose, A-pose, standing and seating
- Inter Pose Datasets: has walking and running
- Hard Pose Datasets: includes all the postures with high complexity like yoga, push-ups, and activities
Our project is available at https://github.com/DoanDuyVo/DeepLab_Human
Research paper is also available at https://github.com/DoanDuyVo/DeepLab_Human/blob/main/DeepLab_Human_Paper.pdf
The original version of DeepLabCut can be found step-by-step in the user guide at https://github.com/DeepLabCut/DeepLabCut
Here are the results of project:
Figure 1: Images from evaluation results
Figure 2: The graphs plot the trajectories
Click on the images to watch video which are created with trailpoints and draw_skeleton
If you are new to DeepLabCut, here are resources we recommend before jumping in.
- Please first read at the Nature Protocol paper;
- Check out the quick video on navigating the docs;
- Check out the free DeepLabCut Course: we have put together a "course" on the science of DeepLabCut and how to use it.
There are the links to all the key steps to get you up and running within a day
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If you use this code or data provided by DeepLabCut, we kindly as that you please cite Mathis et al, 2018 and, if you use the Python package (DeepLabCut2.x) please also cite Nath, Mathis et al, 2019.
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If you use our dataset, images or run code provided by this project, please cite Doan.V & Butler.T, 2021