A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

Overview

HOW TO USE THIS PROJECT

A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

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

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Figure 2: The graphs plot the trajectories

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Click on the images to watch video which are created with trailpoints and draw_skeleton

Watch the video

More resources for help

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

References:

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