Human Pose Estimation Using OpenCV

Overview

Human Pose Estimation is a cutting-edge computer vision task that involves identifying and tracking key points (joints) on the human body. This project leverages OpenCV and pre-trained deep learning models to detect and visualize human poses in images or videos. It is an ideal starting point for those interested in applications like fitness tracking, sports analysis, human-computer interaction, and augmented reality.

Features

• Detect and map key points of the human body (e.g., head, shoulders, elbows, knees).
• Generate skeletal representations by connecting key points.
• Process real-time video streams or static images.
• Highly customizable and easy to integrate with other projects.

Technologies Used

• Programming Language: Python
• Libraries: OpenCV, NumPy
• Pre-Trained Models: OpenPose, MediaPipe, or any custom deep learning model for pose estimation