Real-time human action recognition from aerial videos using autozoom and synthetic data

Ruiqi Xian; Bryan I. Vogel; Celso M. De Melo; Andre V. Harrison; Dinesh Manocha

doi:10.1117/12.3013547

7 June 2024 Real-time human action recognition from aerial videos using autozoom and synthetic data

Ruiqi Xian, Bryan I. Vogel, Celso M. De Melo, Andre V. Harrison, Dinesh Manocha

Author Affiliations +

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 130350I (2024) https://doi.org/10.1117/12.3013547
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In this paper, we propose a novel approach for real-time human action recognition (HAR) on resource-constrained UAVs. Our approach tackles the limited availability of labeled UAV video data (compared to ground-based datasets) by incorporating synthetic data augmentation to improve the performance of a lightweight action recognition model. This combined strategy offers a robust and efficient solution for UAV-based HAR.We evaluate our method on the RoCoG v2¹ and UAV-Human² datasets, showing a notable increase in top-1 accuracy across all scenarios on RoCoG: 9.1% improvement when training with synthetic data only, 6.9% with real data only, and the highest improvement of 11.8% with a combined approach. Additionally, using an X3D backbone further improves accuracy on the UAV-Human dataset by 5.5%. Our models deployed on a Qualcomm Robotics RB5 platform achieve real-time predictions at approximately 10 frames per second (fps) and demonstrate a superior trade-off between performance and inference rate on both low-power edge devices and high-end desktops.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruiqi Xian, Bryan I. Vogel, Celso M. De Melo, Andre V. Harrison, and Dinesh Manocha "Real-time human action recognition from aerial videos using autozoom and synthetic data", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130350I (7 June 2024); https://doi.org/10.1117/12.3013547

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