Analyzing Motion Tracking Algorithms Based on Machine Learning

This paper presents a comparative study of motion tracking algorithms based on machine learning. Solutions such as OpenPose and AlphaPose are examined. Each of these tools employs different approaches and methods for recognizing and tracking human movements, making them valuable in computer vision and deep learning tasks. The paper delves into their architectural features, accuracy, processing speed, and applicability in real-world scenarios. The experiments conducted help identify the strengths and weaknesses of each approach and provide recommendations for selecting the most suitable solution for developing motion tracking systems based on project requirements.