Human pose estimation using blaze pose.

Pose estimation is a fascinating aspect of pattern recognition that is used in a variety of industries, such as technology, healthcare, gaming, etc. The goal of human posture estimation is to foretell the positions of joints and body components in still photos and moving pictures. Given the wide range of uses for such a system, it constitutes one of the most intriguing domains of computer vision research and has had significant growth. The compact convolutional neural networks architecture for human posture estimation proposed in this study, called BlazePose, is intended towards mobile device legitimate prediction. During analysis, the network operates from over 30 fps on something like a Pixel 2 mobile, providing 33 body keypoints for such a specific individual. As a result, it is particularly well-suited for real-time applications like as fitness tracking and action recognition comprehension. This paper makes two major contributions: a novel body posture tracking approach and a lightweight body pose estimation computational model that evaluates parameters utilizing heatmaps and regression. The suggested method has improved estimate accuracy, which is useful when it is used for robotics, augmented reality, sports and fitness, action recognition, motion analysis, and human-computer interface. [ABSTRACT FROM AUTHOR]