Keypoint_Communities
openpifpaf
Keypoint_Communities | openpifpaf | |
---|---|---|
1 | 1 | |
267 | 1,129 | |
- | 0.7% | |
2.6 | 3.6 | |
7 months ago | 11 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Keypoint_Communities
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[R] Keypoint Communities
github: https://github.com/duncanzauss/keypoint_communities
openpifpaf
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Human Pose Estimation Recommendation
OpenPifPaf is decent, easier to install on newer systems than OpenPose imo.
What are some alternatives?
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
human-action-classification - This repository allows you to classify 40 different human actions. Pose detection, estimation and classification is also performed. Poses are classified into sitting, upright and lying down.
lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
posenet-python - A Python port of Google TensorFlow.js PoseNet (Real-time Human Pose Estimation)
tensorflow_Realtime_Multi-Person_Pose_Estimation - Multi-Person Pose Estimation project for Tensorflow 2.0 with a small and fast model based on MobilenetV3
AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.
kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.