Abstract
Skeleton sequences are light-weight and compact, and thus ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion, to boost recognition performance. The use of first- and second-order features, i.e., joint and bone representations has led to high accuracy, but many models are still confused by actions that have similar motion trajectories. To address these issues, we propose fusing third-order features in the form of angles into modern architectures, to robustly capture the relationships between joints and body parts. This simple fusion with popular spatial-temporal graph neural networks achieves new state-of-the-art accuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced run time. Our sourcecode is publicly available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2105.01563
