Ethan Dell Demonstrates TensorFlow Lite Pose Detection on a Raspberry Pi 4 with GPIO Trigger Board

Building on earlier work by Evan Juraes, Dell shows how easy it is to get started with TensorFlow Lite computer vision on the Raspberry Pi.

Developer Ethan Dell has released a video showcasing how to get started using TensorFlow Lite to run a pose estimation model on a Raspberry Pi 4 single-board computer, building on earlier work by Evan Juraes.

"[Pose estimation is] a technique that uses machine learning models to determine the pose of a person — basically how they're standing — and this is done through using machine learning techniques to determine where key body part points are," Dell explains.

Dell shows how well TensorFlow Lite pose estimation can run on the Raspberry Pi 4 Model B 4GB. (📷: Ethan Dell)

"The goal of this project was to [see] how well pose estimation could perform on the Raspberry Pi. Google provides code to run pose estimation on Android and iOS devices — but I wanted to write Python code to interface with and test the model on the Pi."

Dell's project runs on a Raspberry Pi 4GB, the middle-model in a family which extends from an entry-level 2GB - following the launch 1GB model's semi-retirement — to a top-end 8GB, with the Raspberry Pi Camera Module and a small breadboard with an LED, current-limiting resistor, and push button.

The actual work of estimating the pose of users within the camera's field of vision is handled by Google's TensorFlow Lite PoseNet model: When the button, connected to the Raspberry Pi's general-purpose input/output (GPIO) header, is pushed still frames are captured, analysed, and saved with an skeleton overlaid showing how the model tracked the user's motion and pose.

The full video is available on Dell's YouTube channel, while the source code — based on an earlier release by Evan Juraes — can be found on GitHub under an unspecified license.

Gareth Halfacree
Freelance journalist, technical author, hacker, tinkerer, erstwhile sysadmin. For hire:
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