'''Pose estimation''' is a computer vision technique that predicts {| class="wikitable sortable"|-! Sr No !! Model !! Developer !! Key Points !! Source !! License|-| 1 || OpenPose || Carnegie Mellon University || Detecting key points of the configuration of a person's or object's joints or parts in an image or videohuman body, including hand, facial, and foot || [https://github. com/CMU-Perceptual-Computing-Lab/openpose OpenPose GitHub] || MIT
It involves detecting and tracking the position and orientation of these parts, usually represented as keypoints. Pose estimation is widely used in applications such as motion capture, human-computer interaction, augmented reality, and robotics. The process typically involves training machine learning models on large datasets of annotated images to accurately identify and locate the keypoints. These models can range from simple algorithms for 2D pose estimation to more complex systems that infer 3D poses. Recent advances in deep learning have significantly improved the accuracy and robustness of pose estimation systems, enabling their use in real-time applications. '''MediaPipe''' is an advanced computer vision tool developed by Google, designed to accurately identify and track human poses in real-time. MediaPipe leverages machine learning to detect and map out keypoints on the human body, such as the elbows, knees, and shoulders, providing a detailed understanding of body posture and movements.|}
