It involves detecting and tracking Pose estimation is a computer vision technique that predicts the position and orientation configuration of these a person's or object's joints or parts, usually represented as keypointsin an image or video.

Pose estimation is widely used in applications such as motion capture, human-computer interaction, augmented reality, and robotics. The process typically It involves training machine learning models on large datasets of annotated images to accurately identify detecting and locate tracking the position and orientation of these parts, usually represented as keypoints.

These models can range from simple algorithms for 2D pose Pose estimation to more complex systems that infer 3D posesis widely used in applications such as motion capture, human-computer interaction, augmented reality, and robotics. Recent advances in deep The process typically involves training machine learning have significantly improved models on large datasets of annotated images to accurately identify and locate the accuracy and robustness of pose estimation systems, enabling their use in real-time applicationskeypoints.

'''=== Pose Estimation Related Models === {| class="wikitable sortable"|-! Sr No !! Model !! Developer !! Key Points !! Source !! License|-| 1 || MediaPipe''' is an advanced computer vision tool developed by || Google|| Tracking 33 key points on the human body, designed to accurately identify and track human poses in realoffering cross-timeplatform, customizable ML solutions || [https://github. com/google/mediapipe MediaPipe leverages machine learning to detect GitHub] || Apache 2.0|-| 2 || Detectron2 || Facebook AI Research || High-performance codebase for object detection and map out keypoints on segmentation, including pose estimation || [https://github.com/facebookresearch/detectron2 Detectron2 GitHub] || Apache 2.0|-| 3 || OpenPose || Carnegie Mellon University || Detecting key points of the human body, such as the elbowsincluding hand, kneesfacial, and shouldersfoot || [https://github.com/CMU-Perceptual-Computing-Lab/openpose OpenPose GitHub] || MIT|-| 4 || MoveNet || Google Research || Detecting 17 critical key points of the human body || [https://github.com/tensorflow/tfjs-models/tree/master/posenet MoveNet GitHub] || Apache 2.0|-| 5 || PoseNet || Google Research || Detecting different body parts, providing comprehensive skeletal information || [https://github.com/tensorflow/tfjs-models/tree/master/posenet PoseNet GitHub] || Apache 2.0|-| 6 || DCPose || Deep Dual Consecutive Network || Detecting human pose from multiple frames, addressing motion blur and occlusions || [https://github.com/DeepDualConsecutivePose/dcpose DCPose GitHub] || MIT|-| 7 || DensePose || Facebook AI Research || Mapping human-based pixels from an RGB image to the 3D surface of a detailed understanding of human body posture || [https://github.com/facebookresearch/DensePose DensePose GitHub] || Apache 2.0|-| 8 || HigherHRNet || HRNet || Addressing scaling differences in pose prediction, especially for shorter people || [https://github.com/HRNet/HigherHRNet HigherHRNet GitHub] || MIT|-| 9 || Lightweight OpenPose || Daniil-Osokin || Real-time inference with minimal accuracy drop, detecting human poses through key points || [https://github.com/Daniil-Osokin/lightweight-human-pose-estimation Lightweight OpenPose GitHub] || MIT|-| 10 || AlphaPose || MVIG-SJTU || Detecting multiple individuals in various scenes, achieving high mAP on COCO and movementsMPII datasets || [https://github.com/MVIG-SJTU/AlphaPose AlphaPose GitHub] || MIT|}  [[File:Pose detection overlay.gif|720px|thumb|Mediapipe Pose Detection]] <gallery>Pose_example1.png|About to StandPose_example2.png|Standing but error in leg detectionPose_example3.png|Foreground missedPose_example4.png|Hoodie</gallery> [[File:Poseoutput white orig.gif|720px|thumb|Mediapipe Pose Detection]]