# A Person Re-Identification Method Based on Multi-Branch Feature Fusion
## 4 Experiments
### Experimental Results  
Table 1 shows the Rank-1 and mAP comparisons of our MFFNet on the Market1501 [43], DukeMTMC-reID [44], and MSMT17 [45] datasets. Our approach yields a better performance than several other SOTA methods on these three datasets.  
On Market1501, we achieve 95.2% and 85.3% on Rank-1 and mAP, respectively, and Rank-1 performs better than the other methods. For the DukeMTMC-reID, we achieve 92.5% and 80.6% on Rank-1 and mAP, respectively, which are significantly better than the performance of other methods. Additionally, on the MSMT17, which is closest to a real scene, MFFNet also exerts its structural advantages. Compared with CDNet, which has the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Venue**} & \multicolumn{2}{c}{**Market-1501**} & \multicolumn{2}{c}{**DukeMTMC-reID**} & \multicolumn{2}{c}{**MSMT17**} \\ \cline{3-8}  & & **Rank-1/\%** & **mAP/\%** & **Rank-1/\%** & **mAP/\%** & **Rank-1/\%** & **mAP/\%** \\ \hline SVDNet [31] & ICCV 17 & 82.3 & 62.1 & 76.7 & 56.8 & - & - \\ PCB [32] & ECCV18 & 92.3 & 77.4 & 81.7 & 66.1 & - & - \\ PCB+RPP [32] & ECCV18 & 93.8 & 81.6 & 83.3 & 69.2 & - & - \\ MHN [26] & CVPR19 & 95.1 & 85.0 & 89.1 & 77.2 & - & - \\ Auto-ReID [30] & ICCV19 & 94.5 & 85.1 & 88.5 & 75.1 & 78.2 & 52.5 \\ OSNet [8] & ICCV19 & 94.8 & 84.9 & 88.6 & 73.5 & 78.7 & 52.9 \\ HOReID [33] & CVPR20 & 94.2 & 84.9 & 86.9 & 75.6 & 78.4 & 54.8 \\ SNR [28] & CVPR20 & 94.4 & 84.7 & 84.4 & 72.9 & - & - \\ CDNet [29] & CVPR21 & 95.1 & 86.0 & 88.6 & 76.8 & 78.9 & 54.7 \\ L3 DS [27] & CVPR21 & 95.0 & 87.3 & 88.2 & 76.1 & - & - \\ FED [25] & CVPR22 & 95.0 & 86.3 & 89.4 & 78.0 & - & - \\ IER+MTOR [22] & IEEE22 & 90.2 & 75.5 & 82.6 & 66.4 & - & - \\ FPTE [34] & Springer23 & 94.3 & 87.0 & 88.7 & 77.2 & - & - \\ MFFNet & This work & 95.2 & 85.8 & 92.5 & 80.6 & 80.2 & 56.6 \\ \hline \hline \end{tabular}  
* The best performances in each dataset are highlighted in red, whereas the second-best is highlighted in blue.  
\end{table}
Table 1: Comparison of Rank-1 and mAP performance between our approach and several state-of-the-art methods on the Market1501, DukeMTMC-reID, and MSMT17 datasets.  
best performance of Rank-1, and the HOReID, which has the best performance of mAP, it still increases by 1.3% and 1.8%, respectively. Overall, our model yields better performance, thus demonstrating the superiority and effectiveness of our approach.  
In order to compare the performance of the model more intuitively, we visualize the recognition results on the Market-1501 dataset and compare them with the results from other methods. These visualizations are presented in Figures 6-8. Notably, MFNet demonstrates excellent recognition efficacy and robustness, particularly under illumination differences, scale changes, and occlusion scenes. In each row, the first column of images represents the target pedestrian for the query, while the subsequent ten columns depict the recognition outcomes sorted based on similarity. The green solid line indicates that the pedestrian in the image corresponds to the queried pedestrian ID, whereas the red dashed line indicates a different pedestrian ID from that of the query.  
Additionally, we employ Grad-CAM [47] to generate heat maps to visualize the features extracted by each model. The visualization results are presented in Table 2, where the color gradient from blue to red indicates the feature weight values ranging from the lowest to the highest. MFNet can focus on pedestrian targets more completely and accurately, basically covering the entire pedestrian area. At the same time, it has better anti-interference ability in complex scenes.  
Figure 6: Visualization results of PCB on Market-1501.  
Figure 7: Visualization results of HOReID on Market-1501.