# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 4 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline
**SI. No.** & **Method** & **mAP (\%)** & **R-1 (\%)** & **R-5 (\%)** \\ \hline
1 & SLSR [47] & 65.13 & 91.24 & NR \\ \hline
2 & VANeT [48] & 66.34 & 89.78 & 95.99 \\ \hline
3 & Batch Sample [49] & 67.55 & 90.23 & 96.42 \\ \hline
4 & Part-regularized near duplicate [25] & 74.30 & 94.30 & 98.70 \\ \hline
5 & MRL + Softmax Loss [50] & 78.50 & 94.30 & 98.70 \\ \hline
6 & MRM [23] & 68.55 & 91.77 & 95.82 \\ \hline
7 & SAN [27] & 72.5 & 93.3 & 97.1 \\ \hline
8 & TCPM [34] & 74.59 & 93.98 & 97.13 \\ \hline
9 & DGPM [35] & 79.39 & 96.19 & 98.09 \\ \hline
10 & AttributeNet [51] & 80.1 & 97.1 & 98.6 \\ \hline
11 & PGAN [52] & 79.30 & 96.5 & 98.30 \\ \hline
12 & Swin Transformer [53] & 78.6 & 97.3 & NR \\ \hline
13 & TransReid [54] & 78.2 & 96.5 & NR \\ \hline
14 & VehicleGAN [55] & 74.2 & 93.6 & 97.3 \\ \hline
15 & URRNet [39] & 72.2 & 93.1 & 97.1 \\ \hline
16 & MIMA Net [16] & 79.89 & 94.99 & 98.81 \\ \hline
17 & CFSA [45] & 79.89 & 94.99 & 98.81 \\ \hline
18 & GLSIPNet + RR (Ours) & 80.99 & 95.7 & 97.55 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods on the VeRi dataset. NR: not reported. In Table 1, we can see that the proposed method consistently outperformed the baseline across all metrics. Re-ranking significantly improved accuracy on the VeRi dataset but resulted in only a slight improvement on the VehicleId dataset. This is due to the dataset structure of VehicleId, which contains only one image per vehicle identity in the gallery set, in contrast to VeRi, which contains many images per vehicle identity. Also, the VehicleId dataset includes only two camera viewpoints, whereas VeRi includes 20. The Euclidean distance is therefore sufficient for the VehicleId dataset, and nearest-neighbor extrapolation does not have any additional advantage. Also, it can be seen that the improvement due to re-ranking on the VeRi dataset was more pronounced for GLSIPNet than for the baseline  
We present a comparison of the performance of our proposed method with that of several state-of-the-art methods for vehicle re-identification in Tables 2 and 3. The comparison includes different methods that focus on global and part/local feature-based learning, adopting various strategies. SLSR uses a DenseNet121 backbone for feature extraction. It also uses a generative network to generate fake images at different scales for better feature learning [48]. VANeT addresses the problem of different viewpoints [49]. The method learns the viewpoint of the image--front/rear--along with deep features using a GoogleNet backbone. The idea of sampling information data points was explored for vehicle re-identification in a batch sample through the introduction of a triplet sampling variant [50]. Part regularized near duplicate is a framework for combining local region-of-interest-based features and global features [25]. First, the different parts of the vehicle, like windows and lights, are extracted using a part extraction network. Then, a two-branch model is used, where the local branch learns deep features from the local parts generated by the earlier network, and a global branch learns the global features using the ResNet50 backbone. MRL divides the problem of re-identification into two groups on the basis of view--one of images with the same view and the other with different views. Further, on a ResNet 50 backbone, with global features, it uses two types of ranking loss to optimize the training process [50]. Local feature extraction is performed in detail in MRM, where a separate labeled dataset is created to train a model for local feature/attribute extraction [23]. The model is then used to extract the features from the re-identification dataset and then merge them with the global features generated using a ResNet backbone. A part-based network with horizontal partitions, along with a global branch, was introduced in SAN [27].