U-ActionNet: Dual-Pathway Fourier Networks With Region-of-Interest Module for Efficient Action Recognition in UAV Surveillance

Unmanned Aerial Vehicles (UAV) have revolutionized human action recognition by offering a bird's-eye perspective, thereby unlocking unprecedented potential for comprehensive support within surveillance systems. This paper presents synergistic strategies for enhancing Human Action Recognition (HAR) in UAV imagery. Leveraging a dual-path approach, we propose a novel framework U-ActionNet that integrates FFT-based substance separation and space-time self-attention techniques to improve the accuracy and efficiency of HAR tasks. Catering the server side the first pathway employs a modified C3D model with Fast Fourier Transform (FFT)-based object movement and attention detection mechanisms to effectively extract human actors from complex scenes and capture spatiotemporal dynamics in UAV footage. Moreover, a generalized Region-of-Interest (ROI) module is utilized to concentrate on optimal regions to enhance target recognition. Through extensive experiments on the Drone Action and UAV-human datasets, we demonstrate the effectiveness of our approach, achieving superior performance compared to the state-of-the-art methods with Top-1 accuracies of 94.94% and 95.05%, respectively. Meanwhile, the second pathway employs edge-optimized models, integrating ROI extraction-based frame sampling technique to eliminate static frames while preserving pivotal frames essential for model training. We introduce a lightweight model named U-ActionNet Light which is a combination of MobileNetV2, Fourier module, and BiLSTM models that demand only one-ninth of the parameters compared to the server-side model. Demonstrating its efficacy, this model attains Top-1 accuracies of 80.43% and 84.74% on the same datasets, surpassing the baseline by a significant margin. Experimental results show that the presented frameworks are promising for surveillance, search and rescue, and activity monitoring, where accurate and real-time human action recognition from UAV platforms is essential.