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This project is a deep learning framework built for detecting and tracking human body keypoints in images and video streams. It functions as both a real-time motion tracking system and a machine learning environment for training and evaluating pose estimation models.
The main features of tensorboy/pytorch_realtime_multi-person_pose_estimation are: Pose Estimation, Pose Estimation Frameworks, Vector Field Estimation, Human Pose Detections, Pose Estimation Training Pipelines, Computer Vision Model Trainers, Greedy Keypoint Grouping, Deep Learning Training Toolsets.
Projects with overlapping indexed features include: zhec/realtime_multi-person_pose_estimation — This is a multi-person pose estimation framework designed for real-time human keypoint detection. It functions as a… nvidia-ai-iot/trt_pose — This project is a computer vision framework designed for the real-time detection of human body keypoints and skeletal… mvig-sjtu/alphapose — AlphaPose is a deep learning pose estimation framework and PyTorch computer vision library designed for detecting and… deeplabcut/deeplabcut — DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion… cmu-perceptual-computing-lab/openpose — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot… freemocap/freemocap — FreeMoCap is an open-source markerless motion capture system that reconstructs 3D human pose from video. It uses a…
This is a multi-person pose estimation framework designed for real-time human keypoint detection. It functions as a bottom-up human pose estimator that identifies skeletal joints across all people in a scene without requiring a separate person detector. The system utilizes a convolutional neural network model to generate heatmaps and vector fields for posture analysis. It specifically implements part affinity fields to encode the location and orientation of limbs, allowing the model to connect individual joints into complete skeletons. The project covers computer vision motion analysis and d
This project is a computer vision framework designed for the real-time detection of human body keypoints and skeletal structures. It provides an integrated toolkit for training, optimizing, and executing pose estimation models specifically for deployment on edge computing hardware. The framework distinguishes itself by utilizing part affinity field mapping to encode spatial relationships between joints, which are then processed through a greedy parsing algorithm to reconstruct human skeletons from visual data. To ensure high-performance execution, the library incorporates model quantization a
AlphaPose is a deep learning pose estimation framework and PyTorch computer vision library designed for detecting and tracking human body, face, hand, and foot keypoints in images and videos. It provides a system for skeletal posture estimation and multi-person pose tracking. The project implements tools for three-dimensional human pose reconstruction, generating joint positions and body mesh shapes from two-dimensional image data. It also includes a multi-person pose tracker capable of maintaining the identity of multiple people across consecutive video frames. The framework covers a broad
DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion tracking system that identifies anatomical keypoints on animals in video sequences without the need for physical markers. The framework utilizes transfer learning and a library of pre-trained weights to accelerate the training of networks for different species. It supports multi-individual identity tracking to maintain unique identities across video sequences and offers real-time pose detection for live video feeds. The system covers a broad range of computer vision capabilities, i