In the realm of computer vision, OpenPose has emerged as a powerful tool for real-time multi-person detection of human body, hand, facial, and foot keypoints. This article aims to guide you through the training process using the experimental version of OpenPose.
Contents
Introduction
OpenPose is the first real-time system capable of jointly detecting multiple human keypoints across various categories. It consists of 135 keypoints identified from single images, an impressive feat that has made it a go-to solution for many researchers and developers. Developed by a team of dedicated contributors, this repository includes both training code and experimental models that haven’t yet made their way into the main OpenPose framework.
Functionality
- Training Code: Implements the training mechanisms for OpenPose.
- Experimental Models: Includes models not part of the official OpenPose offering, such as BODY_135 and BODY_25B.
Testing
For extensive testing procedures, refer to the testing README.
Training
The training process involves multiple scripts that facilitate the creation of necessary files and execute the training itself. You can find further details in the training README.
Understanding Training with an Analogy
Imagine training OpenPose as teaching a child how to recognize different kinds of fruits. Initially, the child might only know what an apple looks like. As you show them more fruits like bananas, oranges, and strawberries, their understanding improves and they become more adept at identifying not just apples, but various fruits based on characteristics such as shape, color, and size.
In the same way, during the training phase, we provide the model with numerous labeled images of poses (the ‘fruits’) so that it learns to detect various human positions and actions from these examples. Just as your child becomes skilled with time and practice, the OpenPose model gets better at recognizing keypoints the more it trains.
Citation
If you find OpenPose valuable for your research, please consider citing the relevant papers in your publications:
@inproceedings{hidalgo2019singlenetwork,
author = {Gines Hidalgo and Yaadhav Raaj and Haroon Idrees and Donglai Xiang and Hanbyul Joo and Tomas Simon and Yaser Sheikh},
booktitle = {ICCV},
title = {Single-Network Whole-Body Pose Estimation},
year = {2019}
}
@inproceedings{cao2018openpose,
author = {Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh},
booktitle = {arXiv preprint arXiv:1812.08008},
title = {OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields},
year = {2018}
}License
OpenPose is available for free non-commercial use and can be redistributed under specific conditions. For commercial purposes, please refer to the FlintBox link.
Troubleshooting
If you encounter any issues while using or training OpenPose, here are a few tips:
- Ensure you’re using compatible systems. This repository has been tested on Ubuntu 16 with CUDA 8 and should work with other versions, though modifications may be necessary.
- If problems persist, refer to community forums or the GitHub issues page for possible fixes.
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