{"id":20839690,"url":"https://github.com/make2explore/raspberrypi-pose-estimation","last_synced_at":"2026-04-22T15:35:01.559Z","repository":{"id":61825651,"uuid":"478949400","full_name":"make2explore/RaspberryPi-Pose-Estimation","owner":"make2explore","description":"Real time pose estimation on Raspberry Pi ","archived":false,"fork":false,"pushed_at":"2022-04-07T14:42:21.000Z","size":14234,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-20T10:01:46.224Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/make2explore.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-04-07T11:13:10.000Z","updated_at":"2024-05-19T09:04:24.000Z","dependencies_parsed_at":"2022-10-21T22:00:17.853Z","dependency_job_id":null,"html_url":"https://github.com/make2explore/RaspberryPi-Pose-Estimation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/make2explore%2FRaspberryPi-Pose-Estimation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/make2explore%2FRaspberryPi-Pose-Estimation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/make2explore%2FRaspberryPi-Pose-Estimation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/make2explore%2FRaspberryPi-Pose-Estimation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/make2explore","download_url":"https://codeload.github.com/make2explore/RaspberryPi-Pose-Estimation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243196664,"owners_count":20251861,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-18T01:14:12.451Z","updated_at":"2025-12-24T15:58:09.998Z","avatar_url":"https://github.com/make2explore.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"**[Project] Real Time Body Pose Estimation on Raspberry Pi using TensorFlow Lite**\n\nThis application estimates a person's pose in a scene. The deep learning model used recognizes elbows, knees, ankles, etc.  \n-  \u003cem\u003e A fast C++ implementation of TensorFlow Lite Posenet on a bare Raspberry Pi 4 64-bit OS.\u003c/em\u003e  \n\n-  \u003cem\u003e Once overclocked to 1825 MHz, the app runs at 9.4 FPS without any hardware accelerator. \u003c/em\u003e  \n\n\u003cbr\u003e  \n\n---------------------------------------------------------------------------------------------------------\n\n**Prerequisites**  \n- Installation of OpenCV from source                    🔗  [https://bit.ly/3xbB3Jk]  \n- Installation of Code::Blocks IDE in Raspberry pi OS  \n\u003e $ sudo apt-get install codeblocks  \n- Tuneup your Raspberry Pi for Vision based projects - ▶️  [https://youtu.be/00c2GTpRaU8]  \n\nOR  \n\n- 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!  \n\n![output image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find this example on Q-engineering's [SD-image](https://github.com/Qengineering/RPi-image)\n\n\u003cbr\u003e\n\n----------------------------------------------------------------------------------------------------------\n**Object Detection main code Citations** -  \n\nSource code --\u003e   \n\n📎 Created by [Q-engineering](https://qengineering.eu)  \n    \n📎 Referred by make2explore 2022/03/24  \n\n\u003cbr\u003e  \n  \nSource Code Credits ❤️ -  Thank you - [Q-engineering](https://qengineering.eu)  ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmake2explore%2Fraspberrypi-pose-estimation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmake2explore%2Fraspberrypi-pose-estimation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmake2explore%2Fraspberrypi-pose-estimation/lists"}