Deep Learning-Based Recognition and Visualization of Human Motion Behavior

Abstract

Human behavior recognition refers to the classification task of identifying the specific actions of human characters based on the characteristics of human body and the completed actions through a specific algorithm. It has a wide range of applications in intelligent surveillance, video retrieval and so on. The main challenge in this direction is to accurately extract the semantic information of each behavior to describe its dynamic changes in space and time. Therefore, this article introduces the latest research progress in the field of human behavior recognition. Through deep learning techniques, particularly convolutional neural networks and recurrent neural networks, human movements in video data can be effectively identified. However, deep learning models lack interpretability, which can be a challenge in practical applications. The researchers also introduce the application of traditional methods and deep learning-based methods to human behavior recognition, and explore the advantages of deep learning models in processing multi-time scale information and introducing attention mechanisms. Finally, the paper summarizes the potential of deep learning technology combined with multimodal data in behavioral analysis, and provides prospects for applications in smart fitness, health care and other fields.