Workout Pose Estimation using OpenCV and MediaPipe
We live in an era of a digital world where we see everything through a digital glass. Technology has taken over the simplest to earnest tasks for us. The need for another person to be present around us for a simple task is getting slim to none.
Computer vision and deep learning are the basis of what we call our modern era digital monitoring. Whether it is security, learning, or daily lifestyle activities, computer vision helps us monitor correct outcomes without human effort. With cutting-edge technologies in computer vision, we can detect, monitor, and even control the results as we desire.
One of the crucial parts of computer vision is the human body pose estimation. Estimating an entire body includes detecting limb and joints and eliminating light, clothing, and image noise. To do this on top of a live video stream adds to the challenge. Here MediaPipe and OpenCV come into action.
We will discuss what MediaPipe and OpenCV are, how they smooth up the process of human body pose estimation, and how exactly they work. This article will discuss the usage of these technologies to create a tool that helps individuals exercise properly without trainers.
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What is OpenCV?
OpenCV (Open-source Computer Vision) is an open-source library. It provides a range of tools and services for image processing, extraction, and manipulation. The library is cross-platform and supports Windows, Linux, and macOS along with some other platforms. Some OpenCV applications that help in Pose Estimation include Motion Tracking, Gesture Detection, and Structure from Motion.
What is MediaPipe?
MediaPipe is also an open-source framework that provides compact, ready-to-use, fast machine learning solutions for live stream media. It provides machine learning solutions – known as pipelines, for cross-platform in multiple languages. Some ML solutions in MediaPipe include facial recognition, iris movement, holistic, pose-estimation, and face mesh.