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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 main features of nvidia-ai-iot/trt_pose are: Human Pose Detections, Pose Estimation Training Pipelines, Vision Detection Model Training, Deep Learning Motion Tracking Toolkits, Fine-Tuning Dataset Loaders, TensorRT Engine Compilation, Edge AI Model Deployment, Pose Estimation Frameworks.
Projects with overlapping indexed features include: tensorboy/pytorch_realtime_multi-person_pose_estimation — This project is a deep learning framework built for detecting and tracking human body keypoints in images and video… 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… leoxiaobin/deep-high-resolution-net.pytorch — This project is a PyTorch implementation of a research architecture designed for high-resolution representation… deeplabcut/deeplabcut — DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion… hrnet/higherhrnet-human-pose-estimation — HigherHRNet is a deep learning framework designed for bottom-up human pose estimation. It functions as a computer… roboflow/rf-detr — RF-DETR is a Python library for training and deploying object detection, instance segmentation, and keypoint detection…
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 system utilizes a two-branch convolutional neural network to predict body part locations and their directional connections simultaneously. It employs multi-stage feature refinement to improve keypoint localization accuracy and uses greedy parsing and bipartite matching algorithms to associate detected parts into individual sk
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 PyTorch implementation of a research architecture designed for high-resolution representation learning. It serves as a computer vision framework focused on precise keypoint detection, human pose estimation, and semantic image segmentation. The implementation provides specialized tools for identifying anatomical landmarks on the human body and predicting facial keypoint coordinates to analyze orientation and alignment. It utilizes a system of multi-resolution parallel streams and repeated multi-scale fusion to maintain high-resolution representations throughout the network.
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