# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.1 Feature-Level Fusion  
In this experiment, we explored the impact of feature-level fusion techniques on the performance of various backbone architectures. We selected three distinct architectures, each with unique characteristics, to generalize our findings effectively. This involved two primary approaches, which are tested in the following sub-sections. To this end, we employed two primary fusion approaches, namely direct concatenation and PCA-concatenation. These were chosen for their potential to integrate complementary information effectively, thereby improving the robustness and accuracy of the identification process. Concatenation:  
Table 1 illustrates the enhancements achieved by concatenating gait features, with a dimensionality of 128, into various appearance feature extraction architectures. Each model was optimized with a specific concatenation factor (\(\lambda\)), as detailed in Equation (7). Feeding the tested models the 128-dimensional gait feature vector output by the GaitGraph architecture resulted in the following noticeable improvements for all models:  
* OSNet: Initially achieving a rank-1 accuracy of 59.20% with its compact 512-dimensional architecture, the accuracy of OSNet increased by 11.73% following the integration of this 128-dimensional gait feature vector. * MGH: For the MGH model, which produces 5120-dimensional vectors, the integration of the 128-dimensional gait features increased the rank-1 accuracy by 4.7%. * PiT: The PiT model, known for its extensive 9216-dimensional output vector, showed an initial rank-1 accuracy of 92.07%. This accuracy was increased by 1% following the integration of the gait feature vector. These results demonstrate the value of integrating gait features, especially the 128-dimensional representation output by the GaitGraph architecture, in enhancing the performance of various appearance feature backbones in person re-identification (Re-ID) tasks. This integration not only improves upon existing methods but also pushes the state of the art. PCA Concatenation:  
Our experimental results, as presented in Table 2, clearly demonstrate the enhancement achieved by applying PCA with concatenation as a fusion technique in person Re-ID. This approach was employed after normalizing the dimensionality of appearance features to match the 128 gait features using principal component analysis (PCA). PCA played a crucial role in transforming the appearance features derived from various backbone architectures into a uniform 128-dimensional format, which is essential for ensuring a balanced and efficient fusion process. For each studied model, we found the optimal value for the fusion process. The results obtained with the different tested models are described below. * OSNet: The OSNet architecture, with an original rank-1 accuracy of 59.20%, achieved a score of 74.00% when fused using PCA with concatenation, corresponding to an important increase of 14% attributed to the effective integration of the normalized gait and appearance features. This significant improvement demonstrates the potential of this real-time method. * MGH: The MGH model, initially achieving a rank-1 accuracy of 85.60%, reached an accuracy score of 90.07% post fusion. This enhancement underscores the benefit of applying PCA for dimensionality normalization before the fusion process. * PiT: PiT, starting with a high rank-1 accuracy of 92.07%, showed a slight increase to reach 92.87%. This demonstrates the impact of feature fusion on already high-performing architectures. These results highlight the positive impact of balancing dimensionality with concatenation in integrating gait and appearance features for person re-identification.