Pose Detection in Twilio Video with TensorFlow.js

Pose detection is being performed in a Twilio Video application using TensorFlow.js and the PoseNet model, allowing for the detection of various poses such as hands, elbows, and wrists. The code uses machine learning to estimate the confidence level of each pose and draw the corresponding keypoints on an HTML canvas over a video feed. A skeleton is also drawn between related points in the keypoints array returned by the PoseNet model. The application allows users to join or leave a video room and view their own poses being detected in real-time, with the option to share it with friends. The completed code can be found on GitHub, and this project serves as a stepping stone for building more complex applications such as games or apps that utilize pose detection.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.