https://github.com/make2explore/raspberrypi-pose-estimation

Real time pose estimation on Raspberry Pi
https://github.com/make2explore/raspberrypi-pose-estimation

Last synced: 3 months ago
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Real time pose estimation on Raspberry Pi

• Host: GitHub
• URL: https://github.com/make2explore/raspberrypi-pose-estimation
• Owner: make2explore
• Created: 2022-04-07T11:13:10.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-04-07T14:42:21.000Z (almost 4 years ago)
• Last Synced: 2025-02-20T10:01:46.224Z (about 1 year ago)
• Language: C++
• Size: 13.6 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
**[Project] Real Time Body Pose Estimation on Raspberry Pi using TensorFlow Lite**

This application estimates a person's pose in a scene. The deep learning model used recognizes elbows, knees, ankles, etc.  

-   A fast C++ implementation of TensorFlow Lite Posenet on a bare Raspberry Pi 4 64-bit OS.  

-   Once overclocked to 1825 MHz, the app runs at 9.4 FPS without any hardware accelerator.   


  

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**Prerequisites**  

- Installation of OpenCV from source                    🔗  [https://bit.ly/3xbB3Jk]  

- Installation of Code::Blocks IDE in Raspberry pi OS  

> $ sudo apt-get install codeblocks  

- Tuneup your Raspberry Pi for Vision based projects - ▶️  [https://youtu.be/00c2GTpRaU8]  

OR  

- You can just download **SD image** of a Raspberry Pi 4 with **pre-installed frameworks and deep-learning examples**. This image is created by [Q-engineering](https://qengineering.eu). Find a complete working Raspberry Pi 4 dedicated to deep learning on following GitHub page link. Download the **zip file** from GDrive site, unzip and **flash the image** on a 16 GB SD-card, and enjoy!  

![output image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find this example on Q-engineering's [SD-image](https://github.com/Qengineering/RPi-image)




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**Object Detection main code Citations** -  

Source code -->   

📎 Created by [Q-engineering](https://qengineering.eu)  

    

📎 Referred by make2explore 2022/03/24  


  

  

Source Code Credits ❤️ -  Thank you - [Q-engineering](https://qengineering.eu)