Human Pose Estimation with Deep Learning – A Complete Guide

September 03, 2021

Human pose estimation – a computer vision (CV) technique combined with deep learning – a subset in artificial intelligence development can accurately predict and track the location of a given person or an object by going through the different pose combinations using machine learning services to identify the exact pose and the orientation of the same.

software development company with its artificial intelligence development expertise can help implement accurate human pose estimation. By making use of deep learning and machine learning services a camera relative inference of a person or object from either images or video can help power up futuristic innovations with big potential.

And the breakneck speeds at which technology runs paves the way to innovations sprouting from every software development company in the blink of an eye. There are several applications to pose estimation technology – for estimating various human activities, robotics, augmented reality (AR), virtual reality (VR) entertainment, fitness, and more.

We’ll dive more into human pose estimation with our below guide to give you the best overall about the same.

How does Human Pose Estimation Work?

Human pose estimation requires several key points (representation of major elbow, knee, wrist joints, etc.) for accurately estimating the poses. The flexibility or the arm/leg bending capability in humans are different in which the key points will help identify the poses and positions of humans. The basic principle uses machine and deep learning services and runs on convolutional neural networks (CNNs). It processes RGB (aka normal, regular) which has easy implementation as handheld devices are equipped with in-built cameras. The other is for infrared (IR) which specifically requires infrared cameras.

Skeleton Recognition

A software development company implementing human pose estimation tracks the position of a human body’s main joints or key points (elbows, wrists, knees, feet, etc.). By inferring the corresponding movements using artificial intelligence development techniques, a skeletal structure can be reconstructed to determine various human postures.

Bottom-Up and Top-Down Approaches

The top-down human pose estimation approach identifies the human first using the machine and deep learning services algorithms. It follows tracking of the human’s key points and puts them inside a virtual box for accurately analyzing their posture.

The bottom-up human pose estimation approach hierarchically groups the various key points (main human body joints), and then to a skeletal structure, to recognize the human body’s position.

Body Model

It uses the artificial intelligence development technique of CNN – with already pre-defined body models to determine the accurate poses. Simple kinematic body models will analyze 13-30 body points, while more comprehensive – mesh body models will analyze hundreds or even thousands of body points using deep and machine learning services algorithms.

Pre and Post-Processing

Pre-processing of human imagery uses a background removal technique to place the identified human body in a virtual box or to simply add body contours. Post-processing has more of a geometrical analysis approach to determine whether the detected human pose is possible or natural.

Multi-person vs. Singular-person Human Pose Estimation

Single-person human pose estimation is far more successful compared to the multi-person approach. If the neural network encounters view obstructions caused by other people or if people are interacting with each other, the neural network will fail to identify people and a successful human posture breakdown will be difficult.

3D vs. 2D Pose Detection

The machine and deep learning services algorithms implemented in 2D pose detection estimate the poses based on X and Y coordinates in RGB images. 3D uses X, Y, and Z coordinates to determine the same and becomes far more challenging than 2D as it depends on the background scene and lighting conditions, and the dataset availability for 3D is also limited.

Images vs. Video

Since the common human pose estimation are done with images, video-based ones are far more advantageous as it breaks down the footage into a series of images for processing to determine/recognize what the poses are. Video-based ones help neural networks have access to dynamic human body changes such as movements or different postures. It gives more visibility to the body parts as opposed to the sometimes hidden scenarios in image-based ones.

Existing Pose Estimation Architectures

Every software development company with their artificial intelligence development can easily help implement the computer vision technique of human pose estimation. Even custom ones aren’t difficult as there are powerful existing architectures or neural networks that can be used or implemented based on specifics. And again, these are tweaked even further to meet the exact requirements anyone is looking for. They are:

  • High Resolution Net (HRNet) majorly deals with problems in image processing for high-resolution representations by considering key points (joints) of a specific person in an image. The one advantage this network has over other existing architectures or networks used for human pose estimation is that it can retain representations in the high resolution itself. The other networks mainly infer from low-resolution representations to match high-resolution posture representations of postures from low-resolution representations to using high-low resolution networks. HRNet is majorly used in televised sports for human pose estimation and detection.
  • OpenPose is an open-sourced, real-time, bottom-up approach used for multi-person human pose estimation with high accuracy detection of key points (joints). OpenPose is very flexible to users as they can choose source images webcams, camera fields, especially from embedded system applications like CCTVs, and has a wide range of hardware architecture support (CUDA GPUs, OpenCL GPUs, or CPU-only devices).
  • DeepCut also has the same approach as OpenPose. It initially detects the total people and predicts the location of their key points (joints). Its applications are mainly for images or videos with multiple persons (football, basketball, and more).
  • Regional Multi-Person Pose Estimation (AlphaPose) uses a top-down approach to estimate poses from inaccurate human bounding boxes. This optimal architecture does human pose estimation via optimally detected bounding boxes. It can detect single or multiple people from the source files.
  • Deep Pose uses deep neural networks to capture all key points (joints), from there where it combines different layers (pooling layer, convolution layer, and a fully-connected layer) to make it whole.
  • PoseNet is a tensorflow.js based human pose estimation architecture compatible with browsers or mobile devices. It can estimate either single or multiple poses.
  • DensePose can map entire human pixels of an RGB image to the 3D surface of the human body for single and multiple human pose estimation problems.

Application Areas

Fitness: Various fitness apps that use artificial intelligence development are created to act as personal trainers based on the person’s body pose or postures detected while working out. It can instruct the person to make their body position optimal or correct. It is a cost-effective approach than hiring a real-life personal coach and minimizes the risk of workout injuries.

Physical Therapy: Similar to fitness apps, physical therapy apps too, detect body postures with the help of machine learning services to provides feedback to users about the specific physical exercises they were doing. This too is an affordable approach than hiring a human physical therapist, as it improves user-health right from the convenience of their homes.

Entertainment: Can be used as a low-cost alternative to costly motion capture systems for filming and video-game production. Its human pose estimation makes the experience in video games more immersive and engaging by tracking player movements and feeding them to their digital counterparts.

Robotics: Movements of robots can be controlled for a more flexible response, minimal recalibration, and quick adaptability irrespective of the environment it is placed.

Human Activity Estimation: Human gestures, postures,activity, movement, etc. are tracked for implementing a broad range of applications such as analysis, fall detection, sports, dancing, security and surveillance enhancement, and more.

Augmented Reality and Virtual Reality: Makes the online user experience better not only for games but for other applications such as military combat training to enhance the combat abilities of soldiers.

Motion Tracking for Consoles: Makes gaming experience highly interactive when the real poses or actions of players are motion tracked in real-time and rendered virtually in the gaming environment by the console.

This is our standard knowledge guide on human pose estimation. We hope you received the best overall about the same and have made you well-informed.