OpenPose Overview, Algorithm and Implementation Examples

Overview of OpenPose

OpenPose is a real-time human posture detection library developed by Carnegie Mellon University’s Persona Computing Center (Perceptual Computing Lab) that can accurately estimate the position of the human body, face, hands, and feet in 3D or 2D. The technology will be used in computer vision and motion detection. This technology is widely used in a variety of fields, including computer vision, motion capture, entertainment, healthcare, and robotics.

OpenPose has the following features

1. Multi-Person Detection: OpenPose can detect multiple people simultaneously, tracking key points on each person’s body, face, hands, and feet. This makes it possible to analyze the movements of multiple people simultaneously.
2. Key Point Detection: Detects the location of key points in 2D or 3D, including the major joints of the human body (shoulders, elbows, knees, hips, etc.). This enables pose analysis and motion analysis.
3. Real-time processing: Fast processing allows real-time estimation of a person’s pose for each frame of video.
4. 3D Pose Estimation: In addition to 2D pose detection, OpenPose supports 3D pose estimation using information from depth cameras and multiple cameras.
5. Flexible applicability: A wide variety of input data types (video, still images, wearable devices, etc.) can be processed, making it a very flexible tool for researchers and developers.
6. Deep learning based: OpenPose uses deep learning (in particular, convolutional neural networks) to perform pose estimation. By using pre-trained models, various postures can be detected with high accuracy.

The main techniques used are

1. Part Affinity Fields (PAF): OpenPose uses a method called “Part Affinity Fields” (PAF) for posture estimation. This method reconstructs the overall posture by representing the relationships between the parts of the human body (hands, arms, legs, etc.). PAF uses edge maps to estimate which parts are connected and how.
2. Convolutional Pose Machines (CPM): CPM will be a method that uses multiple convolutional neural networks (CNN) to perform pose estimation. This improves the accuracy of pose estimation and reduces noise and uncertainty.

OpenPose is a Caffe-based implementation, but interfaces in Python and C++ are also provided. Basically, one can load a model, input an image or video, and get keypoints as output. It is available for many platforms (Linux, Windows, macOS) and developers can easily integrate it using existing APIs.

OpenPose is a very powerful and widely used tool in real-time posture estimation. It utilizes deep learning technology to detect parts of the human body with high accuracy, which is something that is used in many fields, including sports analysis, motion capture, and health management.

Implementation Example

We describe an example implementation using OpenPose, which is implemented primarily in C++, but also supports use in Python and other languages. Here we describe a basic implementation example of 2D pose estimation using OpenPose in the Python environment.

Prerequisites

• Python 3.x
• OpenPose installed (see the official guide for detailed installation instructions)
• Required libraries (numpy, opencv-python, pyopenpose, etc.)

Installing OpenPose

To use OpenPose with Python, you must first build OpenPose in a C++ environment and set up Python bindings. Follow the official installation procedure, and after the build is complete, set it up so that it can be used with Python.

Example Implementation: Pose Estimation from Images

The following code is a simple Python program that uses OpenPose to detect 2D human body poses from images and display the results.

import sys
import cv2
import numpy as np
from openpose import pyopenpose as op

# OpenPose Settings
params = {
    "model_folder": "/path/to/openpose/models/",  # OpenPose model folder path
    "hand": False,  # No hand pose estimation
    "face": False,  # No facial pose estimation
    "number_people_max": 1,  # Set the maximum number of people to 1
}

# OpenPose Setup
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()

# Loading Images
image_path = "your_image.jpg"  # Path of the image to be analyzed
image = cv2.imread(image_path)

# Convert images to OpenPose format
datum = op.Datum()
datum.cvInputData = image

# Perform 2D pose estimation
opWrapper.emplaceAndPop([datum])

# Show Results
output_image = datum.cvOutputData
cv2.imshow("Pose Estimation", output_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Code Description

1. OpenPose setup: In the params dictionary, configure the behavior of OpenPose. model_folder specifies the path to the directory where OpenPose’s trained models are stored, and the hand and face options allow you to decide whether to perform pose estimation for hands and faces. The hand and face options can be set to determine whether or not to perform pose estimation for hands and faces.
2. Initializing the OpenPose wrapper: Create an opWrapper to reflect the settings and start OpenPose with the start() method.
3. Image loading and estimation: The image to be passed to OpenPose is loaded with cv2.imread() and set to datum.cvInputData. Then, pose estimation is performed using opWrapper.emplaceAndPop().
4. Displaying the result: The result of the estimation is stored in datum.cvOutputData, which is displayed using cv2.imshow().

2D Pose Result

When this program is executed, OpenPose detects the 2D human body posture for the specified image and overlays the result on the image. The major joints of the human body (shoulders, elbows, knees, etc.) are detected and the posture connected by lines is displayed on the image.

Execution Method

1. Confirm that OpenPose has been built.
2. Change /path/to/openpose/models/ in the above code to the path where OpenPose is actually installed.
3. Specify the path to the image file (your_image.jpg).
4. When the Python code is executed, the pose estimation result will be displayed on the specified image.

Additional Functions

• Pose detection for multiple people: By changing the value of params[“number_people_max”], you can perform pose estimation for multiple people.
• Pose estimation for hands and faces: Pose detection for hands and faces can also be added by setting the hand and face options to True.

Application Examples

OpenPose is a technology for real-time human pose estimation and has a wide range of applications in various fields. They are described below.

1. Sports performance analysis: OpenPose is used to analyze athletes’ performance. The analysis of sports behavior allows for technical improvements and performance enhancement.

• Example(Baseball Pitching Form Analysis): OpenPose can be used to analyze a pitcher’s pitching form to accurately capture shoulder motion and foot position. This can be used to identify areas for improvement in the pitching form and help improve the player’s performance.
• Example: Soccer running form analysis: By analyzing a player’s running form with OpenPose and measuring the knee angle and foot movement, players can improve their form to prevent injuries.

2. medical and rehabilitation: OpenPose is also used in rehabilitation and orthopedic diagnostics. It enables monitoring of a patient’s movements and evaluation of treatment progress.

• Example(Gait analysis): OpenPose can be used to analyze a patient’s gait pattern and detect abnormal gait at an early stage. It is useful for tracking patient progress during rehabilitation and providing appropriate treatment.
• Example: Supporting recovery of stroke patients Monitoring the movement of patients recovering from a stroke with OpenPose can improve rehabilitation plans and facilitate patient recovery.

3. entertainment industry (film and game production): OpenPose is used in film and game production to realistically represent character movements. In particular, it is used as a complementary technology to motion capture.

• Example(Motion Capture in Movies): OpenPose is used to capture the actions of actors as they perform in real time and apply the data to 3D characters. This improves the quality of film VFX and animation.
• Example: Game Development In game development, OpenPose is also used to detect the player’s movements in real-time and reflect them in the game character’s actions. it is used to improve interactivity in virtual reality (VR) games.

4. interactive technology (entertainment and interface): OpenPose is used in interactive interfaces and games to detect and manipulate user actions.

• Example(Dance games): OpenPose is used to detect the player’s poses in dance games where the user’s body movements are linked to the movements of the in-game characters. The user’s dancing movements can be reflected in the character in real time.
• Example: Control by Hand Gestures Using OpenPose’s hand pose estimation, systems have been developed to control computers and smart appliances by recognizing hand movements and gestures. For example, a user could control the playback or stop of a media player by waving his/her hands.

5. Surveillance and Security: OpenPose is also used in security systems that analyze the movements and postures of people in real time from surveillance video to detect the movements of suspicious persons.

Example(Detection of suspicious behavior):  OpenPose can be used to analyze a person’s movement and posture from surveillance camera video to detect unusual or suspicious behavior. For example, this could be a system that warns in real time of a suspicious person wandering about.

6. education and exercise support: OpenPose is also useful in the fields of education and fitness. By analyzing posture and body movements, OpenPose helps users improve their performance.

Example(Yoga Pose Analysis): In a yoga or Pilates class, OpenPose can be used to check if the student’s postures are correct and provide feedback. Can evaluate postures in real time and teach areas for improvement.
Example: home exercise instruction An application that helps users exercising at home to check their posture and train properly through OpenPose.

7. Fashion and virtual fitting: OpenPose is also being applied in the fashion industry. It is used to accurately capture the user’s body posture and to perform virtual fitting.

Example(Virtual fitting): A service that allows customers to virtually try on clothes that fit their body shape and posture. OpenPose analyzes the user’s body shape and posture and displays in real time how the tried-on garment will look.

reference book

Reference books on OpenPose and related technologies are described below.

1. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Abstract: A comprehensive textbook on deep learning, covering everything from basic concepts to the latest research, this book will help lay the groundwork for understanding deep learning-based technologies such as OpenPose.

2. “Computer Vision: Algorithms and Applications” by Richard Szeliski

Summary: A good resource for understanding posture estimation techniques (such as OpenPose) with a book covering computer vision basics and applications. It provides background knowledge of computer vision technology.

3. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

Summary: A practical approach to machine learning and deep learning, this book is rich in implementation examples using TensorFlow and Keras, and provides a good understanding of deep learning models such as OpenPose.

4. “Deep Learning for Computer Vision” by Rajalingappaa Shanmugamani

Abstract: This is a textbook on deep learning with a special focus on computer vision. Particularly rich in content related to tasks like image classification, object detection, and pose estimation; a good reference for understanding OpenPose implementations.

5. “PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research

6. “Deep Learning with Python” by François Chollet

Abstract: This book by François Chollet, developer of Keras, is ideal for learning practical implementations of deep learning. It provides a good base knowledge for implementing posture estimation models in Keras.

7. “Convolutional Neural Networks for Visual Recognition” by Fei-Fei Li, Andrej Karpathy, Justin Johnson

Abstract: This material is useful for understanding Convolutional Neural Networks (CNNs) and provides the basics of CNNs used in OpenPose.

8. “Real-Time Pose Estimation with Python and OpenCV: A Hands-on Guide“