People detection has long been a popular issue in traditional object detection for a spectrum of uses. Computers can now interpret human body language by performing stance recognition and pose tracking thanks to recent advances in machine-learning techniques. The precision of these detections, as well as the technology needs to execute them, have now reached a commercially viable level. Furthermore, the technology’s development is being deeply influenced by the coronavirus pandemic, where high performance real-time posture detection and tracking will usher in some of the most impactful advances in computer vision.
Combining human position estimation and distance projection algorithms, for example, can be used for social distancing. In a busy environment, it helps people maintain physical space from one another. When we analyze how pose estimate may be used to automatically detect human movement, we can see how powerful it is.
Pose estimation has the potential to develop a new wave of automated systems meant to quantify the precision of human movement, from virtual sports coaches and AI-powered personal trainers to tracking movements on factory floors to ensure worker safety. Autonomous driving is one area where this technology has already proven its worth. Computers can identify and predict pedestrian behavior more accurately with the use of real-time human pose detection and tracking, allowing for more consistent driving
Moreover, the experiment compares and contrasts the BlazePose and MoveNet frameworks in the tackling of the Pose Estimation problem.