# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.2 Score-Level Fusion  
The effectiveness of weighted averaging as a score-level fusion strategy is underscored by the notable performance improvements in person Re-ID, as detailed in Table 3. This approach strategically combines the scores from different models, assigning weights that reflect their relative confidence, leading to a cumulative enhancement of the overall identification accuracy. * OSNet: The integration of gait data via weighted averaging boosted OSNet's performance by 11.00%, reaching a rank-1 accuracy score of 70.66%. * MGH: The performance of the MGH model, which achieved a rank-1 accuracy of 85.60% using only appearance features, reached 90.20% using weighted averaging. * PiT: For the PiT model, known for its high initial rank-1 accuracy of 92.07%, the application of score-level fusion with weighted averaging further boosted its performance, achieving a rank-1 accuracy of 93.20%. Although PiT achieves high scores by using only appearance features, we can see that the addition of gait features can also increase its performance. These results show the efficiency of weighted averaging in score-level fusion for person Re-ID. This approach, which integrates both appearance and gait features, increased the accuracy scores of all tested models. The impact of implementing threshold-based conditional fusion on person Re-ID is documented in Table 4. This advanced approach combines gait information with appearance features in a dual-case strategy; it either fuses the two data types or selects the superior model based on appearance according to a predetermined threshold. This methodology consistently enhanced the accuracy of person Re-ID across all tested architectures. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**Appearance**} & \multicolumn{4}{c}{**Appearance + Gait**} \\  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 59.20 & 82.60 & 89.34 & 94.80 & 74.00 (+14.20) & 91.94 (+9.34) & 95.67 (+6.33) & 98.47 (+3.67) \\ MGH [7] & 85.60 & 97.10 & 99.00 & 99.30 & 90.07 (+4.47) & 98.54 (+1.44) & 99.34 (+0.34) & 99.80 (+0.50) \\ PiT [10] & 92.07 & 98.93 & 99.80 & 100 & 92.87 (+0.80) & 99.40 (+0.47) & 99.94 (+0.14) & 100 (+0.00) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Person Re-ID performances (rank-1/rank-5/rank-10/rank-20) in the iLIDS-VID dataset according to the model with or without the integration of PCA concatenation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**Appearance**} & \multicolumn{4}{c}{**Appearance + Gait**} \\  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 59.20 & 82.60 & 89.34 & 94.80 & 70.66 (+11.46) & 89.93 (+7.33) & 94.40 (+5.06) & 97.53 (+2.73) \\ MGH [7] & 85.60 & 97.10 & 99.00 & 99.30 & 90.20 (+4.60) & 98.53 (+1.43) & 99.00 (+0.00) & 99.60 (+0.30) \\ PiT [10] & 92.07 & 98.93 & 99.80 & 100 & 93.20 (+1.13) & 99.26 (+0.33) & 99.86 (+0.06) & 100.00 (+0.00) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Person Re-ID performances (rank-1/rank-5/rank-10/rank-20) on the iLIDS-VID dataset according to the model with or without the integration of score-level fusion with weighted averaging. OSNet: The OSNet model, originally achieving a rank-1 accuracy of 59.20%, reached a score of 70.26% using threshold-based conditional fusion. * MGH: The MGH model, which recorded an initial rank-1 accuracy of 85.60%, saw its performance boosted to 89.66%. This demonstrates the substantial impact of integrating gait data to improve the accuracy of high-dimensional feature vectors. * PiT: For the PiT model, known for its extensive output vector, the initial rank-1 accuracy of 92.07% increased to 93% after fusion. This increase, albeit modest, underscores the value of fusion even in models with high baseline performance.