Analysis of Human Action Recognition with Deep Learning

Human Action Recognition (HAR) is a critical research area in computer vision, with notable applications in security surveillance, health monitoring, and human-computer interaction. This study explores the latest advancements in deep learning techniques applied to HAR over the past decade. This study also presents a comprehensive framework that incorporates various advanced methodologies, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) enhanced with Long Short-Term Memory (LSTM) and attention mechanisms, Graph Convolutional Networks (GCN), and Transformer architectures. The paper discusses the architecture and principles of these models, analyzing their advantages and disadvantages in various tasks. Moreover, the study identifies current challenges and proposes future directions for enhancing the performance of HAR systems. Key areas for future research include integrating sensor data with visual information to improve recognition accuracy, reducing computational complexity, and increasing real-time applicability in practical systems. This research provides an in-depth overview of the current status of HAR technology and sets the stage for further advancements and applications, serving as a foundation for future research in the field.