# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 5 Results and Analysis
### Comparison Experiments  
To comprehensively evaluate the effectiveness of the proposed MNTReID model, it is compared with several state-of-the-art algorithms in the field of pedestrian re-identification. The models included in this comparison are OSNet [14], ABDNet [15], AlignedReID [13], Cluster Contrast REID [33], and SVDNet [12] et al. These algorithms are evaluated on both the PRAI-1581 dataset and this paper's dataset. The results of these comparisons are presented in Tables 3 and 4. As shown in the experimental results in Tables 3 and 4, MNTReID achieves an mAP of 58.1% and a Rank-1 accuracy of 63.7% on the PRAI-1581 dataset. On the dataset presented in this paper, MNTReID achieves an mAP of 88.9% and a Rank-1 accuracy of 94.8%, outperforming existing models. Specifically, MNTReID improves Rank-1 by 6.2% on this study's dataset and by 16.5% on the PRAI-1581 dataset compared to OSNet. While OSNet \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mAP/\%** & **Rank-1/\%** \\ \hline OSNet [14] & 79.7 & 88.6 \\ ABDNet [15] & 81.9 & 90.5 \\ AlignedReID [13] & 78.1 & 88.3 \\ TransREID [28] & 79.4 & 89.9 \\ Cluster Contrast REID [33] & 52.1 & 49.6 \\ MNTReID & 88.9 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods on the dataset of this paper. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **FLOPS/G** & **Parameter/M** & \multicolumn{2}{c}{**Self-Constructed Dataset**} & \multicolumn{3}{c}{**PRAI-1581**} \\ \cline{3-8}  & & **mAP/\%** & **Rank-1/\%** & **Rank-10/\%** & **mAP/\%** & **Rank-1/\%** & **Rank-10/\%** \\ \hline Baseline & 101.2 & 96.8 & 79.4 & 89.9 & 95.7 & 50.7 & 56.2 & 67.1 \\ +Multi-scale Fusion & 124.6 & 102.6 & 84.1 & 92.9 & 97.0 & 52.4 & 60.1 & 67.4 \\ +GCN & 134.3 & 105.8 & 83.4 & 90.9 & 97.2 & 51.6 & 61.8 & 63.4 \\ +MU-GCN & 141.6 & 108.8 & 85.9 & 93.3 & 96.8 & 55.8 & 63.8 & 74.6 \\ +Non-attention mechanism & 137.5 & 106.5 & 82.3 & 91.2 & 95.9 & 53.7 & 62.9 & 71.8 \\ MNTReID & 143.2 & 110.1 & 88.9 & 94.8 & 97.4 & 58.5 & 70.9 & 83.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different methods on the dataset of this paper and PRAI-1581 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mAP/\%** & **Rank-1/\%** \\ \hline OSNet [14] & 42.1 & 54.4 \\ SVDNet [12] & 36.7 & 46.1 \\ AlignedReID [13] & 37.6 & 48.5 \\ Pretrained VTT [39] & 57.3 & 65.3 \\ Cluster Contrast REID [33] & 21.8 & 23.5 \\ LTReID [38] & 58.7 & 66.3 \\ Meta-Learning [30] & 38.1 & 64.9 \\ GCCReID [29] & 25.5 & 31.3 \\ Subspace Pooling [36] & 39.6 & 49.8 \\ MGAiA [40] & 42.7 & 55.3 \\ MNTReID & 58.5 & 70.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different methods on PRAI-1581 dataset. effectively captures multi-scale global features through full-scale feature learning and demonstrates strong global feature extraction capabilities, MNTReID not only optimizes the extraction of global features but also enhances the processing of local information through MU-GCN. This combination results in improved performance in aerial imagery. Although AlignedReID excels in processing local pedestrian features, its ability to integrate global information is limited. In contrast, MNTReID achieves notable improvements in mAP, with increases of 9.5% on this paper's dataset and 20.9% on the PRAI-1581 dataset. This superior performance is attributed to the integration of the non-local attention module, which substantially enhances robustness in complex scenarios. Compared to TransREID, MNTReID demonstrates significant performance enhancements, achieving improvements of 24.7% in Rank-1 accuracy and 18.8% in mAP on the PRAI-1581 dataset. This improvement is attributed to the synergistic combination of MU-GCN and the non-local attention module, which enhances the model's capability to handle multi-view and complex pedestrian behaviors with higher robustness. By effectively balancing long-range dependencies and local features, overall identification performance is improved.