Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object.
Human Pose Estimation identifies and classifies the poses of human body parts and joints in images or videos. In general, a model-based technique is used to represent and infer human body poses in 2D and 3D space.
The MediaPipe Pose Landmarker task lets you detect landmarks of human bodies in an image or video. You can use this task to identify key body locations, analyze posture, and categorize movements. This task uses machine learning (ML) models that work with single images or video. The task outputs body pose landmarks in image coordinates and in 3-dimensional world coordinates.
MediaPipe Pose is a single-person pose estimation framework. It uses BlazePose 33 landmark topology. BlazePose is a superset of COCO keypoints, Blaze Palm, and Blaze Face topology. It works in two stages – detection and tracking
Framework: mediapipe
1. Git clone this repo.
2. Cd to POSE folder.
3. Run `ergonomy.ipynb` notebook.