GYM REP TRACKER USING MEDIAPIPE AND PYTHON

Abstract

Human Pose estimation has grabbed the eye of the computer vision community for the past few decades. It is a vital step closer to knowledge people in pics and motion pictures. Strong articulations, small and hardly visible joints, occlusions, apparel, and lighting changes make it very difficult to perform estimate pose. Human Pose estimation is an important problem that needed to be study. It is used to detect human anatomical key points (e.g., shoulder, elbows, legs, wrist, etc.) in real time using less computational resources. There are many Artificial Intelligence models i.e, Posenet, OpenPose1 and MediaPipe8 for Real time Human Pose Estimation. Many experiments has performed to find out the best suitable model for Human Pose Estimation. Experiments stated that PoseNet is suitable to run on lightweight devices like browsers whereas OpenPose meant to run on GPU powered devices and is more accurate. On the other hand, MediaPipe is very fast, modular, reusable and highly efficient. Hence, our model uses the MediaPipe to perform its estimation. Keywords: Pose estimation, Gym Rep Tracker, Media Pipe, Python, Machine learning