Adaptive Refined Graph Convolutional Action Recognition Network with Enhanced Features for UAV Ground Crew Marshalling

For unmanned aerial vehicle (UAV) ground crew marshalling tasks, the accuracy of skeleton-based action recognition is often limited by the high similarity of motion patterns across action categories as well as variations in individual performance. To address this issue, we propose an adaptive refined graph convolutional network with enhanced features for action recognition. First, a multi-order and motion feature modeling module is constructed, which integrates joint positions, skeletal structures, and angular encodings for multi-granularity representation. Static-domain and dynamic-domain features are then fused to enhance the diversity and expressiveness of the input representations. Second, a data-driven adaptive graph convolution module is designed, where inter-joint interactions are dynamically modeled through a learnable topology. Furthermore, an adaptive refinement feature activation mechanism is introduced to optimize information flow between nodes, enabling fine-grained modeling of skeletal spatial information. Finally, a frame-index semantic temporal modeling module is incorporated, where joint-type semantics and frame-index semantics are introduced in the spatial and temporal dimensions, respectively, to capture the temporal evolution of actions and comprehensively exploit spatio-temporal semantic correlations. On the NTU-RGB+D 60 and NTU-RGB+D 120 benchmark datasets, the proposed method achieves accuracies of 89.4% and 94.2% under X-Sub and X-View settings, respectively, as well as 81.7% and 83.3% on the respective benchmarks. On the self-constructed UAV Airfield Ground Crew Dataset, the proposed method attains accuracies of 90.71% and 96.09% under X-Sub and HO settings, respectively. Environmental robustness experiments demonstrate that under complex environmental conditions including illumination variations, haze, rain, shadows, and occlusions, the adoption of the Test + Train strategy reduces the maximum performance degradation from 3.1 percentage points to within 1 percentage point. Real-time performance testing shows that the system achieves an end-to-end inference latency of 24.5 ms (40.8 FPS) on the edge device NVIDIA Jetson Xavier NX, meeting real-time processing requirements and validating the efficiency and practicality of the proposed method on edge computing platforms.