Pedestrian Attribute Recognition Model for UAV Application: A Practical Approach
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Abstract
Pedestrian Attribute Recognition (PAR) is an important component of intelligent surveillance systems. Ground-to-aerial cross-domain PAR and the effect of UAV flight conditions remain largely unexplored. This work investigates whether PAR models trained on ground-level CCTV datasets can be applied to UAV imagery and quantitatively analyzes the impact of UAV elevation angle and horizontal distance on attribute recognition performance. Five CNN models are trained on two CCTV datasets with different characteristics: UPAR, a large and diverse dataset, and TPAD, a homogeneous dataset, using a multi-label classification framework with positive class weighting to handle class imbalance. Cross-dataset evaluation on CCTV data leads to the selection of RegNet and ConvNeXt. For ground- to-aerial evaluation, selected models are evaluated on three UAV datasets: UAV-Human, AG-VPReID, and a self-collected UAV-PT1 dataset, where UPAR-trained models achieve a mean attribute accuracy of 62.23-65.48%, while TPAD-trained models perform worse. RegNet achieves comparable performance to ConvNeXt with significantly lower computational complexity, making it more suitable for UAV deployment. Attribute-level analysis shows that UpperBodyLength, LowerBodyLength, LowerBodyColor, and Backpack are more reliably recognized. Further analysis using UAV-PT1 shows increasing the horizontal distance from 25 m to 50 m reduces accuracy by 11.3712.11%, and a high elevation angle of 50◦causes a significant performance drop, providing an evaluation of ground-to-aerial PAR and the impact of UAV flight parameters on attribute recognition.
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