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

Last update: Jan 10, 2022

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Deep Learning DeepLab_Human

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

Figure 2: The graphs plot the trajectories

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

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:

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.
If you use our dataset, images or run code provided by this project, please cite Doan.V & Butler.T, 2021

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Comments

CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

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

Related tags

Overview

HOW TO USE THIS PROJECT

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

More resources for help

References:

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