# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset
#### 5.2.3 Comparison with State-of-the-Art Methods  
This section evaluates GAF-Net's performance against current state-of-the-art methods on the MARS dataset. MARS presents significant challenges for person re-identification systems due to its scale, complexity, and variability. Our approach employs the MGH architecture [7] as the appearance module backbone, incorporating gait characteristics through the use of PCA conditional concatenation. Table 10 provides a comparison of GAF-Net with recent Re-ID methodologies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Appearance**} & \multicolumn{5}{c}{**Appearance + Gait**} \\  & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 81.64 & 86.20 & 95.71 & 96.96 & 97.99 & 81.71 & 86.20 & 95.76 & 97.07 & 97.99 \\ MGH [7] & 85.52 & 89.40 & 96.63 & 97.72 & 98.42 & 85.57 & 89.46 & 96.68 & 97.72 & 98.42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of score-level fusion on the MARS dataset(Threshold-Based Conditional Fusion).  
Several important observations can be drawn from these results. Our GAF-Net, using MGH as the backbone with PCA conditional concatenation, achieved competitive performance with, an mAP of 86.09% and a rank-1 accuracy of 89.78%. These results, while solid, are slightly behind those of some of the most recent and best-performing methods. Among the recent top-performing approaches, DenseIL [8] achieves an mAP of 87% and a rank-1 accuracy of 90.8%, outperforming our method by 0.91% in mAP and 1.02% in rank-1 accuracy. Similarly, PIT [10] obtains comparable results, with a mAP of 86.8% and a rank-1 accuracy of 90.22%. DCCT [11], another recent method, shows even higher performance, with a mAP of 87.5% and a rank-1 accuracy of 92.3%, surpassing our GAF-Net by 1.41% in mAP and 2.52% in rank-1 accuracy. These results indicate that approaches based on transformers and hybrid CNN-transformer architectures can offer significant advantages for person re-identification. Nevertheless, our GAF-Net compares favorably to other recent methods. For example, our method slightly outperform STRF [50] in terms of mAP (86.09% versus 86.10%) while remaining close in rank-1 accuracy (89.78% versus 90.30%). Similarly, we obtained comparable results to those of PSTA [9] and AA-RGTCN [49], which achieve mAPs of 85.8% and 85.9%, respectively.  
The results indicate that GAF-Net maintains robust performance for higher ranks, with a rank-5 accuracy of 96.90%, rank-10 accuracy of 98.04%, and rank-20 accuracy of 98.59%. These results are competitive or superior to those reported for other methods, demonstrating the robustness of our approach.  
In conclusion, although our GAF-Net does not achieve the best performance among the most recent methods on the MARS dataset, it remains competitive, demonstrating the effectiveness of our approach fusing appearance and gait characteristics. These results suggest avenues for future improvement, potentially by incorporating elements from the best-performing methods, such as PiT [10] or DenseIL [8], into GAF-Net.