Human Pose Estimation: A Key Technology for Computer Vision

What is Human Pose Estimation?

Human Pose Estimation (HPE) is a method of identifying and classifying the joints of the human body.

Basically, it’s a way to capture a set of coordinates for each joint (arm, head, torso, etc.), known as key points, that can represent a person’s pose. Connections between these points are called pairs. Connections formed between points must be significant. That is, not all points can form pairs.

From the beginning, HPE’s goal has been to create skeletal images of the human body and further process them for task-specific applications.

There are Three Types of Approaches to Modeling the Human Body

1. Skeleton-based model
2. Contour-based model
3. Volume-based model

Why does Human Pose Estimation Matter?

Pose estimation allows you to track an object or person in real space in incredible detail. This powerful feature enables a wide range of applications.

Pose estimation differs from other everyday computer vision tasks in several important ways. Tasks like object detection also find objects in images. However, this localization is usually coarse-grained and has a bounding box around the object. Pose estimation goes further to predict the exact location of key points associated with the object.

One can imagine the power of pose estimation when considering its application to automatic human motion tracking. From virtual athletic trainers and AI-powered personal trainers to motion tracking on the factory floor to ensure worker safety, pose estimation is one of the most automated tools designed to measure the accuracy of human motion. It has the potential to create new waves.

What is the Skeleton-based Model?

Skeleton-based models are most commonly used in human pose estimation due to their flexibility. This is because it consists of a series of joints such as ankles, knees, shoulders, elbows, wrists, and limbs that make up the skeletal structure of the human body.

Skeleton-based models are used for both 2D and 3D representations. However, as a rule, we use a combination of 2D and 3D methods. 3D human pose estimation takes depth coordinates into account and incorporates these results into the calculations, improving the measurement accuracy of your application. Depth is important for most movements because the human body does not move in her 2D dimension.

How Does 3D Human Pose Estimation Work?

The overall flow of a pose estimation system starts with collecting initial data and uploading it for processing by the system. Since we are dealing with motion detection, we need to analyze a series of images rather than a static image. Because we need to extract how the key points change in the movement pattern.

After the image is uploaded, the HPE system recognizes and tracks key points required for analysis. So different software modules are responsible for tracking 2D key points, creating body representations, and transforming them into 3D space. So when we talk about creating a model for estimating pose, we usually mean implementing two different modules for 2D and 3D planes.

Therefore, for most human pose estimation tasks, the flow is divided into two parts.

1. Detects and extracts 2D key points from an image sequence. Use horizontal and vertical coordinates to build the skeleton structure.
2. Add depth dimension to convert 2D key points to 3D.

During this process, the application performs the calculations necessary to perform pose estimation.

2D vs 3D Pose Estimation

Building on the original 2D approach, 3D human pose estimation predicts and accurately identifies the positions of joints and other important points in three dimensions (3D). This approach provides extensive 3D structural information for the entire human body. 3D pose estimation has many applications, including 3D animation, augmented and virtual reality creation, and behavior prediction.

Of course, 3D pose animation takes longer. Especially when the commentator has to spend more time manually labeling essential points in her 3D. One of the most popular solutions that circumvent many of the challenges of 3D pose estimation is OpenPose, which uses neural networks for real-time annotation.

What are the Most Popular Machine Learning Models for Estimating Human Pose?

1. OmniPose

We propose OmniPose, an end-to-end one-pass trainable framework that achieves state-of-the-art results in multi-person pose estimation. Using a novel waterfall engine, the OmniPose architecture leverages multi-scale feature representations that increase the effectiveness of backbone feature extractors without the need for post-processing.

OmniPose integrates cross-scale contextual information and joint localization with Gaussian heatmap modulation in a multi-scale feature extractor to estimate human pose with state-of-the-art accuracy. The multi-scale representation obtained by OmniPose’s improved waterfall engine exploits the progressive filtering efficiency of the cascade architecture while maintaining a multi-scale field of view comparable to spatial pyramid configurations.

2. OpenPose

OpenPose is a popular bottom-up machine learning model for tracking, inferring, and annotating multiple people in real-time. An open-source algorithm ideal for detecting key points on faces, bodies, feet, and hands.

OpenPose is an API that allows easy integration with various CCTV cameras and systems, and a lightweight version is ideal for edge devices.

3. MediaPipe

MediaPipe is an open-source cross-platform and customizable ML solution for live and streaming media” developed and provided by Google. MediaPipe is a powerful machine learning model built for facial recognition, hands, poses, real-time eye tracking, and general use. The Google AI and Developers blog has many in-depth Google use cases, and he hosted several MediaPipe meetups in 2019 and 2020.

4. DeepCut

DeepCut is another bottom-up approach that detects multiple people, identifies their joints, and estimates the motion of those joints in an image or video. It is designed to detect the postures and movements of multiple people and is widely used in the field of sports.

5. PoseNet

PoseNet estimates either a single pose or multiple poses, so there is a version of the algorithm that detects only one person in an image/video and another version that detects multiple people. Why are there two versions? There is a single-person pose detector that is faster and simpler, but it requires only one subject to be present in the image.

So this was part one of the blog where I explained what is Human Pose Estimation in the coming week I’ll upload part 2 where I’ll be showing how you can integrate PoseNet with TensorFlow in React.is project.