Human Pose Classification Based on Pose Long Short-Term Memory

Abstract

Objective: At present, pose classification research is a hot topic in the field of computer vision, playing a crucial role in surveillance security, and motion data mining. The research serves as a crucial follow-up to three dimensional (3D) pose estimation and makes a significant contribution to the advancement of artificial intelligence. Pose classification is a complex problem involving a large amount of complicated data, making its digital modeling and further processing challenging. Methods: This article proposes the Pose long short - term memory (LSTM) method to classify 3D poses reconstructed from 2D image sequences. Result: Compared with traditional methods, the experimental tests show that the performance of the Pose LSTM is more superior in all aspects. Conclusion: Pose LSTM is particularly effective for recognizing continuous action poses because they can capture temporal dependencies in sequential data. In the context of pose recognition, it can analyze the sequence of poses over time and understand how these poses transition from one to another, making it possible to accurately classify or predict actions that unfold over a series of frames.