In comparison to the 2D pose detection model, the modified 3D baseline model can produce keypoints with about 780 FPS. In terms of speed, our model achieves about 46 FPS on the above-mentioned hardware without video rendering where the 2D pose detection model produces keypoints with about 50 FPS. In terms of our experiment, we tested the 2D component of a BlazePose with a modified 3D-pose-baseline model using Python language. While models like BlazePose are able to provide real-time processing, the accuracy of its tracking is not suitable for commercial use or complex tasks. So is it possible to improve existing 3D human pose estimation models to achieve acceptable accuracy with real-time processing? In the opposite scenario, the accuracy suffers. Of course, the performance of the model will depend on the chosen algorithm and hardware, but the majority of existing open-source models provide quite a long response time. Whether we deal with a fitness app, an app for rehabilitation, face masks, or surveillance, real-time processing is highly required. Existing 2D detectors can be modified or amplified with the post processing stages to improve general accuracy. One of the possible ways to optimize HPE performance is the acceleration of 2D keypoint detection. Given the fact that BlazePose and VideoPose3D have different 2D detectors, this stage appears to be a performance bottleneck in both cases. The processing time depends on the movement complexity, video and lighting quality, and the 2D pose detector module. VideoPose3D and BlazePose processing results So generally, when we speak about creating a body pose estimation model, we mean implementing two different modules for 2D and 3D planes. In a nutshell, different software modules are responsible for tracking 2D keypoints, creating a body representation, and converting it into a 3D space. Once the image is uploaded, the HPE system will detect and track the required key points for analysis. Since we need to extract how key points change during the movement pattern. As we’re dealing with motion detection, we need to analyze a sequence of images rather than a still photo. The overall flow of a body pose estimation system starts with capturing the initial data and uploading it for a system to process. So now let’s find out how 3D human pose estimation works from a technical perspective, and find out the current capabilities of such systems. For the majority of movements, depth is important, because the human body doesn’t move in a 2D dimension. 3D human pose estimation grants better accuracy to the application measurements since it considers the depth coordinate and fetches those results into calculation. But generally, 2D and 3D methods are used in conjunction. These key points are used to produce a 2D or 3D representation of a human body model.Ī skeleton-based model is used for 2D, as well as 3D representation. The essence of the technology lies in detecting points of interest on the limbs, joints, and even face of a human. This can be used in conjunction with other computer vision technologies for fitness and rehabilitation, augmented reality applications, and surveillance. Most of the HPE methods are based on recording an RGB image with the optical sensor to detect body parts and the overall pose. Human Pose Estimation (HPE) is a task in computer vision that focuses on identifying the position of a human body in a specific scene. Also, we’ll analyze different approaches to Human Pose Estimation as a machine learning technology, and try to define the applications for each. We’ll figure out its principle of work and capabilities to understand suitable business cases. In this article we’ll explore human pose estimation in depth. Implementing such capabilities for a machine results in surprisingly useful applications in different fields. While it sounds awkward, knowing the right angle of a joint in a specific exercise is the basis of work for physiotherapists, fitness trainers, and artists. These points represent our limbs and joints to calculate the angle of flexion, and estimate, well, human pose. Human Pose Estimation is a computer vision-based technology that identifies and classifies specific points on the human body.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |