Human-in-the-Loop

What is Human Pose Estimation?

Joint identification and classification is the goal of Human Pose Estimation (HPE).

This method essentially records a collection of coordinates for each joint that may be used to characterize a person’s stance. A pair is a link between these two locations.

Since not every pair of points can be constructed without a meaningful relationship between them, this is a limiting factor. A human skeleton is the starting point for HPE, which then processes the data for task-specific applications.

Bottom-up and top-down techniques may be used to classify all other methods for estimating poses.

With bottom-up approaches, joint estimates are compiled individually before being combined to generate a posture. Top-down techniques begin with a person detector and, working backward, estimate body joints within the identified bounding boxes.

It is worth mentioning that human pose classification is a subtask of human pose estimation in which an individual’s stance is categorized as one of many possible states, such as “sitting” or “standing”.

3D Human Pose Estimation

The purpose of 3D Human Pose Estimation is to ascertain where a person’s joints will be in 3D space. In addition to the 3D position, certain approaches may also reconstruct a human body mesh from a series of still pictures or a video. Since it may be used to reveal detailed 3D structural information about the human body, this area has garnered a lot of attention in recent years. It has several potential uses, including the animation and AR/VR sectors, as well as 3D action prediction. Monocular photos or videos may be used to estimate a human subject’s 3D posture.

In order to accomplish the very difficult job of 3D pose estimation, information fusion methods may be used to combine data from several cameras or other sensors. While 2D human datasets are readily available, gathering correct 3D posture picture annotations is time-consuming and costly. Thus, while there have been substantial advances in 3D pose tracking in recent years, notably owing to the advances in 2D human pose detection models, there are still a number of obstacles to overcome. These include model generalization, resilience to occlusion, and compute efficiency.

How to model the human body?

The human body may be modeled in three different ways:

• You may utilize the Kinematic Model, often known as a skeleton-based model, to estimate poses in both two and three dimensions. This adaptable and user-friendly human body model represents the human skeleton by means of a collection of joint configurations and limb orientations.
• The 2D posture estimate method is known as the Planar Model or contour-based model. The human body’s look and shape are represented by the planar models. Multiple rectangles approximating human body outlines are the standard method for depicting individual body sections.
• The volumetric model is used in the estimate of a 3D posture. As such, deep learning-based 3D human posture estimation for recovering 3D human mesh relies on a number of popular models.

How does Pose Estimation work?

Several processes make up the foundation of human posture estimation:

• Detection– The first step is to figure out whether there is a person in the shot. To do this, engineers often use convolutional neural networks or sliding-window methods for detecting objects.
• Localization– Once a person has been recognized, the following stage is to localize key points in the human body, such as the joints and other body components. Heatmap regression, component-based models, and CNNs are common tools for this purpose.
• Association– After the key points have been located, they must be linked to the appropriate anatomical structures. In most cases, optimization-based or “greedy” inference methods are used for this purpose.
• Processing– Keypoints are cleaned up and improved in the last stage of processing to increase their precision. Methods like Kalman filtering and non-maximum suppression fall into this category.

Many various designs and methods have been suggested to tackle the Human Pose Estimation problem; some employ convolutional neural networks (CNNs), while others rely on recurrent neural networks (RNNs); some are single-stage, and others are multi-stage. The most up-to-date architectural designs often use a process called “keypoint detection” and “keypoint association,” in which the keypoints are first detected and then associated with the appropriate body sections.

The quality and resolution of the input picture, the existence of occlusions or self-occlusions, the presence of several persons in the image, and the variety of postures and body types may all have an impact on the performance of human pose estimation models.