# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset
#### 5.2.2 Score-Level Fusion  
Building upon the weighted averaging method introduced previously (Equation (9)), we propose a conditional version that accounts for gait availability. For a person's identity (\(i\)), the final score (\(S_{\text{final}}^{i}\)) is defined as follows:  
\[S_{\text{final}}^{i}= \begin{cases}\alpha\cdot S_{\text{gait}}^{i}+(1-\alpha) \cdot S_{\text{appearance}}^{i}&\text{if gait available}\\ S_{\text{appearance}}^{i}&\text{otherwise}\end{cases}  \tag{14}\]  
This approach extends the previous weighted averaging formula with a condition for cases lacking gait features. It increases flexibility, allowing for dynamic adaptation to data availability while maintaining effective score fusion when all information is present  
The efficacy of this method is demonstrated by performance improvements in person re-identification on the MARS dataset, as detailed in Table 8.  
* OSNet: Gait data integration via conditional weighted averaging marginally enhanced OSNet's performance. mAP increased by 0.08% (81.64% to 81.72%). Rank-1 accuracy improved by 0.21% (86.20% to 86.41%). Rank-5 showed a slight decrease (\(-\)0.17%), while rank-10 and rank-20 improved by 0.11% each. * MGH: The conditional fusion slightly improved MGH's performance. mAP increased by 0.08% (85.52% to 85.60%). Rank-1 accuracy improved by 0.27% (89.40% to 89.67%). Rank-5 showed a minor increase (\(+\)0.05%), while rank-10 (97.72%) and rank-20 (98.42%) remained unchanged. These improvements demonstrate the positive contribution of gait information to the MARS dataset. This approach offers an adaptive solution capable of leveraging gait information when available while maintaining performance based solely on appearance in other cases. \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 & 81.89 & 86.52 & 95.87 & 97.01 & 98.10 \\ MGH [7] & 85.52 & 89.40 & 96.63 & 97.72 & 98.42 & 86.09 & 89.78 & 96.90 & 98.04 & 98.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of feature-level fusion on the MARS dataset(PCA conditional concatenation). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Appearance**} & \multicolumn{5}{c}{**Appearance + Gait**} \\
**Backbone** & & **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 & 81.72 & 86.41 & 95.54 & 97.07 & 98.10 \\ MGH [7] & 85.52 & 89.40 & 96.63 & 97.72 & 98.42 & 85.60 & 89.67 & 96.68 & 97.72 & 98.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of score-level fusion on the MARS dataset(Conditional Weighted Averaging). We propose an extension of threshold-based conditional fusion (Equation (10)). This improved method explicitly incorporates a criterion for gait data availability, offering increased adaptability. The score (\(S^{i}_{\text{conditional}}\)) for a person (\(i\)) is determined as follows:  
\[S^{i}_{\text{conditional}}= \begin{cases}w\cdot S^{i}_{\text{appearance}}+(1-w) \cdot S^{i}_{\text{gait}}&\text{if gait available and $S^{i}_{\text{gait}}<$ threshold}\\ S^{i}_{\text{appearance}}&\text{otherwise}\end{cases}  \tag{15}\]  
where \(w\in[0,1]\) is a weight factor for the appearance score, and \"threshold\" determines the reliability of the gait score. This formulation extends our previous approach by considering both gait data availability and quality. It enhances robustness for sequences with absent or unreliable gait information while allowing for fine tuning of each modality's contribution. The method adapts dynamically to each sequence's characteristics, optimizing the use of available information in the MARS dataset.