Human Pose Estimation Using OpenCV
Ch.Yedukondalu Srinivas -, D Sai Anupam -, A Sravan Kumar -, Vorsu karthikeya -, Darvemula Sreeram Krishna · International Journal of Leading Research Publication. · 2025
Human pose estimation is a crucial task in computer vision, involving the detection and tracking of key body joints in images or videos. This technology has numerous applications, including activity recognition, augmented reality, and human-computer interaction.This paper presents an approach to human pose estimation using OpenCV in combination with deep learning-based models such as OpenPose or MediaPipe Pose. The method leverages a pre- trained neural network that detects key body landmarks, such as the head, shoulders, elbows, and knees, from RGB images. OpenCV provides efficient image processing tools to preprocess input frames, detect poses, and visualize the estimated skeletal structure.Our implementation focuses on real time processing by optimizing inference speed while maintaining high accuracy. The proposed system is tested on various datasets to evaluate its robustness under different lighting conditions and human postures. Results demonstrate the effectiveness of OpenCV-based pose estimation in achieving reliable skeletal tracking with minimal computational overhead. This study highlights the potential of OpenCV in real-time human pose estimation and its applications in fitness tracking, gesture recognition, and motion analysis. Future work includes improving model accuracy, reducing latency, and integrating pose estimation with advanced AI- driven applications.