A study of an AI-Based System for Action Recognition and Human Pose Estimation

Abstract The rapid evolution of artificial intelligence has significantly advanced the capabilities of computer vision systems, particularly in the domains of human pose estimation and action recognition. This study examines the design and conceptual development of an AI-driven framework capable of identifying human body postures and recognizing actions from image sequences and video streams in real time. Human pose estimation focuses on detecting anatomically significant joint locations, whereas action recognition involves categorizing observed motion patterns into semantic activity classes. The proposed framework integrates convolutional neural networks for spatial feature extraction, transformer-based architectures for long-range spatiotemporal reasoning, and recurrent neural networks for modeling temporal dependencies. The paper outlines the system architecture, methodological pipeline, dataset selection, preprocessing strategies, and evaluation metrics, while also discussing potential applications in healthcare, intelligent surveillance, and human–computer interaction.