# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 5 Results and Analysis  
### Ablation Experiments  
To comprehensively evaluate the impact of MU-GCN on pedestrian re-identification, this paper conducts ablation experiments on the self-constructed dataset using both ResNet50 and Transformer networks. Additionally, using the Transformer network as a baseline, the study further assesses the effect of the multi-scale graph convolutional  
Figure 7: Selected sample images from this study’s self-constructed dataset. network combined with the non-local attention mechanism on both the self-built dataset and the PARI-1581 dataset. The ablation experiments using the ResNet50 network on the self-constructed dataset are presented in Table 1. These experiments separately evaluate the effectiveness of the multi-scale fusion, GCN, and MU-GCN modules. The inclusion of the multi-scale fusion module results in a 2.2% improvement in mAP. This improvement demonstrates that using convolution kernels of varying scales enables the model to establish stronger correlations between local details and global features. Consequently, the model achieves improved robustness when handling pose variations. In contrast, introducing GCN alone leads to a smaller improvement in both mAP and Rank-1 accuracy. The results are slightly inferior to those achieved with the multi-scale fusion module. This suggests that while GCN enhances feature representation, its ability to capture pedestrian details is more dependent on the quality of the initial features. This dependency contrasts with the multi-scale fusion approach. However, when both the multi-scale fusion and GCN are combined within the MU-GCN module, the two modules complement each other. This synergy leads to a 6.9% increase in mAP and a 3.8% improvement in Rank-1 accuracy. As a result, the model's performance is significantly enhanced, especially in handling complex pedestrian poses. In this study, the TransReID network was used as the baseline model to assess the impact of the MU-GCN and the non-local attention mechanism on model performance. The effects of various pooling methods based on the Transformer network on both the self-constructed dataset and the PARI-1581 dataset are shown in Table 2. Notably, integrating the multi-scale graph convolution network on the self-constructed dataset results in significant performance improvements. Specifically, Rank-1 accuracy and mAP increase by 3.4% and 6.5%, respectively. This demonstrates a substantial enhancement in the model's ability to capture detailed pedestrian features across multiple scales. The improvement is particularly evident in scenarios involving complex pedestrian behavior. Adding a non-local attention mechanism to the PRAI-1581 dataset enhances the model's ability to capture global information. This improvement is particularly effective when the model faces large changes in view angles and when some pedestrian features are scattered across different regions of the image. As a result, the integration of global information is significantly improved. The improvement in mAP and Rank-1 accuracy reaches 58.7% and 62.9%, respectively. Additionally, the model's robustness to changes in pedestrian view angles is markedly enhanced. Finally, the highest accuracy is achieved when both MU-GCN and the non-local attention mechanism are integrated. In this configuration, Rank-1 and mAP are improved by 4.9% and 9.5%, respectively, on the self-constructed dataset. By combining these two mechanisms, the model effectively enhances the extraction of both local and global features. This results in substantial performance improvements in pedestrian re-identification, particularly for complex behaviors in multi-view UAV imagery. Meanwhile, Table 2 compares the performance of different models in terms of computational complexity (FLOPS) and the number of parameters. The baseline model, used as a benchmark, has lower FLOPS