Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery

Pedestrian re-identification in complex scenarios is often hindered by challenges such as viewpoint diversity, background interference, and behavioral complexity, which traditional methods struggle to address effectively in wide-area surveillance. Unmanned Aerial Vehicles (UAVs) offer a promising solution to this problem due to their flexibility and extensive coverage capabilities. However, UAV aerial images introduce additional challenges, including significant viewpoint variations and the complexity of pedestrian behaviors. To address these issues, this paper proposes a Transformer-based model that integrates a multi-scale graph convolution network (MU-GCN) with a non-local attention mechanism to address these challenges. A MU-GCN enhances feature extraction by employing graph convolutional networks to improve feature representation after extracting detailed features at various scales through multi-scale convolution kernels. This strengthens the model’s focus on local information. Meanwhile, the non-local attention mechanism enhances the model’s capacity to capture global contextual information by modeling dependencies between distant regions in the image. This approach is better suited for the unique characteristics of UAV aerial imagery. Experimental results demonstrate that, compared to the baseline model, the proposed method achieves improvements of 9.5% in mean average precision (mAP) and 4.9% in Rank-1 accuracy, validating the effectiveness of the model.