SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION
Abstract
Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features into spatio-temporal matrices. We tested LFHAR on standard datasets to demonstrate its stability and effectiveness. Our method shows significant improvements, achieving 2.7% higher accuracy on NTU-RGB+D 60 and 2.1% higher accuracy on NTU-RGB+D 120 compared to baseline methods. These results confirm that the LFHAR framework effectively improves skeleton-based action recognition performance.