Human pose estimation, a critical task in computer vision, aims to localize key human joints (e.g., shoulders, elbows, knees) from images or videos. This technology has numerous applications, including human-computer interaction, surveillance systems, and sports analysis.
Problem Statement: Accurate and robust human pose estimation in real-world scenarios remains challenging due to factors such as occlusions, varying lighting conditions, and complex backgrounds.
Objectives: This research focuses on developing a high-performance human pose estimation system using machine learning techniques, specifically deep learning. The primary objectives are to:
- Achieve accurate and real-time human pose detection.
- Robustly handle challenging scenarios like occlusions and variations in pose and appearance. Methodology: A deep learning-based approach is employed, utilizing a state-of-the-art convolutional neural network (CNN) architecture. The model is trained on a large dataset of annotated human images, leveraging techniques like data augmentation to improve generalization. Key Results: The developed system demonstrates high accuracy in human pose estimation, achieving competitive performance compared to existing methods. It exhibits robustness to occlusions and variations in pose and appearance. Conclusion: This research successfully demonstrates the effectiveness of deep learning for human pose estimation. The developed system has the potential to be applied in various real-world applications, enabling more intuitive and natural human-computer interaction.