# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 5 Results and Analysis
### Visualization Results  
To intuitively demonstrate the improvements achieved by the proposed model, visualization experiments were conducted on the dataset used in this study. The benchmark network and MNTReID were selected for comparative visualization. In the visualization results, the first image represents the target pedestrian to be queried. The subsequent ten images are arranged in descending order of matching accuracy with the target pedestrian, displaying the top ten query results. Misidentifications are indicated by red boxes. As shown in Figure 8, the first set of pedestrians exhibits dynamic posture changes across multiple viewpoints. The baseline model relies solely on fixed-scale feature extraction, which fails to capture these dynamic changes in pedestrian behavior. This limitation is particularly evident in the eighth and ninth images. Here, the model's lack of sensitivity to local pedestrian features leads to misidentifications. In contrast, MNTReID more effectively captures local features through MU-GCN. Additionally, it integrates global contextual information via the non-local attention mechanism. This integration enables accurate identification of target pedestrians even under dynamic behavior conditions. In the second group, where pedestrians exhibit similar clothing, the baseline model struggles to differentiate the subtle variations between the garments, leading to misidentification. However, MNTReID outperforms the baseline model by capturing the fine-grained differences in clothing through multi-scale feature extraction, effectively avoiding such errors. For the third group, which involves pedestrians with complex behaviors, the baseline model performs reasonably well when the postures remain consistent, accurately identifying pedestrians even when significant posture changes occur. However, the model misidentifies pedestrians in the seventh and ninth frames, where subtle changes in posture and slight clothing differences appear. MNTReID, on the other hand, effectively integrates global contextual information via the non-local attention mechanism, which helps mitigate the interference of environmental factors such as lighting variations. Additionally, by leveraging MU-GCN, MNTReID excels in accurately extracting features in scenarios involving complex pedestrian behavior, ensuring highly accurate identification despite the challenges presented by dynamic environments. In the fourth set of pictures with shadows and pedestrians in complex behavior, the baseline model performs reasonably well when the postures remain consistent, accurately identifying pedestrians even when significant posture changes occur. However, the model misidentifies pedestrians in the seventh and ninth frames, where subtle changes in posture and slight clothing differences appear. MNTReID, on the other hand, effectively integrates global contextual information via the non-local attention mechanism, which helps mitigate the challenges presented by dynamic environments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environmental Condition** & **mAP/\%** & **Rank-1/\%** & **Rank-10/\%** \\ \hline Low light & 81.3 & 89.2 & 96.1 \\ Low resolution & 84.7 & 92.8 & 95.2 \\ Foggy and rainy weather & 84.1 & 92.4 & 96.6 \\ Normal weather & 88.9 & 94.8 & 97.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of the proposed method under various environmental conditions on the dataset of this paper. Figure 8: The visualization results of our proposed MNTReID method are compared with the baseline model on the dataset of this paper. In the example images, the red boxes highlight the identification errors made by the baseline model during the query matching process. In the fifth group of images, the pedestrian is shown performing the action of leaning over a railing. The baseline model struggles to capture detailed information effectively, resulting in misidentification in the ninth image. In contrast, MNTReID, enhanced with the MU-GCN, successfully captures local features.