Temporal-Spatial SPDAGG Network For Skeleton-Based Human Action Recognition From Aerial Perspectives

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Human action recognition with UAVs has garnered high interest due to its significant impact on various fields. This shift necessitates the creation of comprehensive and demanding benchmarks, crucial for the development and assessment of UAV-centric human behavior analysis models. However, the manifold-based approaches in the context of UAV-human action recognition face substantial limitations, given the task’s novelty and inherent complexities.This paper presents a novel approach to UAV-human action recognition, employing skeletal-based features known for their resilience in the face of these challenges. The methodology hinges on a deep neural network capable of capturing the intricate spatial and temporal facets of human actions, resulting in the creation of Semi-Positive Definite (SPD) matrix representations. These SPD representations then serve as the foundation for action classification using a classifier module.To gauge the efficacy of our approach, we conduct rigorous evaluations using the publicly available UAV-Human action recognition dataset and UAV-Gesture dataset. Our results demonstrate the state-of-the-art performance achieved by our method, highlighting its potential to advance UAV-based human action recognition significantly.