Training repository for OpenPose

Modified Caffe version for https://github.com/CMU-Perceptual-Computing-Lab/openpose_train

Keras version of Openpose with optimized model structure and C++ postprocess for acceleration. Trained and tested using CMU handpose dataset.

The core of the system involves implementing pose estimation algorithms using OpenCV, which can accurately locate and track the positions of major joints in the human body. This involves utilizing pre-trained deep learning models, such as PoseNet or OpenPose, to efficiently identify keypoints.

python3, opencv 4, pre-trained openpose model, tensorflow 2

ExerciseDataCollection is the preprocessing system for our BE(Hons) honors project. Automatic system to download, extract, process and create trainable models via skeletal mapping via OpenPose.

based on official caffe_train, i change some cudnn*.cpp and cudnn*.hpp for cuda9.

https://github.com/CMU-Perceptual-Computing-Lab/openpose

Openpose-based personal health trainer

A PyTorch-based virtual try-on system using OpenPose keypoints and pre-trained models for realistic outfit simulation.

Openpose-based personal health trainer

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A similar method to train openpose hand keypoints detection model with caffe and CPM layer.

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Studienarbeit openpose training.

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Learning and Testing with OpenPose

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Train Openpose in keras, and depoly in Python, C++, and Tensorflow.

Intelligent system for analyzing children’s behavior in kindergarten using computer vision and deep learning. Implemented DIFEM to extract motion features from 12 skeletal joints using OpenPose, and trained a Random Forest model achieving 93.6% accuracy.

This project implements Human Pose Estimation using Deep Learning to detect and track human body keypoints in images and videos. It utilizes OpenCV, TensorFlow, and pre-trained models like OpenPose, MediaPipe, or PoseNet for accurate pose detection.

This repository contains the Python files used for the accompanying research in my thesis. The files were created to process Alphapose and Openpose data, create and preprocess datasets, evaluate pose estimations, train and tune RNN, LSTM, and GRU models

PoseMapper is a Python-based software for pose estimation and stickman animation overlay on videos. It uses OpenCV's DNN module with pre-trained OpenPose models to detect human joints and draw a "stickman" figure over the person in the video.

A Python project demonstrating the implementation of various pre-trained deep learning models (GoogLeNet, SSD, OpenPose, Mask R-CNN, YOLOv8) for computer vision tasks using OpenCV's DNN module and the Ultralytics library. It covers image classification, object detection, pose estimation, and instance segmentation on images and videos.