The basic idea of human pose estimation is understanding people’s movements in videos and images. By defining keypoints (joints) on a human body like wrists, elbows, knees, and ankles in images or videos, the deep learning-based system recognizes a specific posture in space. Basically, there are two types of pose estimation: 2D and 3D. 2D estimation involves the extraction of X, Y coordinates for each joint from an RGB image, and 3D - XYZ coordinates from an RGB image.
In this article, we explore how 3D human pose estimation works based on our research and experiments, which were part of the analysis of applying human pose estimation in AI fitness coach applications.
The goal of 3D human pose estimation is to detect the XYZ coordinates of a specific number of joints (keypoints) on the human body by using an image containing a person. Visually 3D keypoints (joints) are tracked as follows:
3D keypoints and their specification (https://mobidev.biz/wp-content/uploads/2020/07/3d-keypoints-human-pose-estimation.png)
Once the position of joints is extracted, the movement analysis system checks the posture of a person. When keypoints are extracted from a sequence of frames of a video stream, the system can analyze the person’s actual movement.
There are multiple approaches to 3D human pose estimation:
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In this article, we explore how 3D human pose estimation works based on our research and experiments, which were part of the analysis of applying human pose estimation.