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 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 can range from simple algorithms on large datasets of annotated images to accurately identify and locate the keypoints. === Pose Estimation Related Models === {| class="wikitable sortable"|-! Sr No !! Model !! Developer !! Key Points !! Source !! License|-| 1 || MediaPipe || Google || Tracking 33 key points on the human body, offering cross-platform, customizable ML solutions || [https://github.com/google/mediapipe MediaPipe GitHub] || Apache 2.0|-| 2 || Detectron2 || Facebook AI Research || High-performance codebase for 2D object detection and 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, including hand, facial, and foot || [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 more complex systems that infer the 3D posessurface of a human body || [https://github.com/facebookresearch/DensePose DensePose GitHub] || Apache 2. Recent advances 0|-| 8 || HigherHRNet || HRNet || Addressing scaling differences in deep learning have significantly improved the 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 and robustness of drop, detecting human poses through key points || [https://github.com/Daniil-Osokin/lightweight-human-pose -estimation systemsLightweight OpenPose GitHub] || MIT|-| 10 || AlphaPose || MVIG-SJTU || Detecting multiple individuals in various scenes, enabling their use achieving high mAP on COCO and MPII 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 real-time applicationsleg detectionPose_example3.png|Foreground missedPose_example4.png|Hoodie</gallery> [[File:Poseoutput white orig.gif|720px|thumb|Mediapipe Pose Detection]]