Human pose estimation is the computer vision task of detecting the positions (coordinates) of key body joints (keypoints) of a person in an image or video, thereby estimating the person’s pose (configuration of the skeleton). Keypoints often include joints like elbows, wrists, knees, ankles, and landmarks like the nose or eyes. There are two common approaches: top-down (first detect the person with a bounding box, then within that box run a pose estimator to find joints) and bottom-up (detect all joint candidates in the image first, then group them into individuals). Popular methods include OpenPose, which uses a bottom-up approach to output keypoints for multiple people, and Mask R-CNN’s keypoint branch (top-down approach). Pose estimation has applications in activity recognition, animation, augmented reality, sports analysis, and human-computer interaction. It’s challenging due to variations in limb articulation, clothing, occlusions, and camera angles.
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