## 4 Experiment
### Ablation Experiment  
(1) Verify the probability map module and the multi-feature fusion module. When neither module is used, the effect is relatively poor, as shown in Table 2 for specific data. When not using the probability map module, the image labels can use the rough estimate label \(Y_{j}\) in Formula (1) to act as pseudo-labels. When the probability graph module is involved, the generated pseudo-label information is relatively accurate. The recognition accuracy of the datasets Markt-1501 and CUHK03 has significantly improved, with Rank-1 in the CUHK03 dataset increasing from 56.4% to 62.3%, an increase of 5.9%. The probability graph module proposed in this article has a positive effect on the recognition accuracy of the model. The binary item constructed using multi-feature fusion information can smoothly generate image labels. With more accurate label supervision, the Re-ID model with higher recognition accuracy can be trained. Similar to proving the effectiveness of the probability graph model, experiments were conducted on the datasets CUHK03 and Markt-1501. The experimental results are shown in Table 2. If the binary items provided by the multi-feature fusion module are involved in the generation of pseudo tags and the training of models, the accuracy of the two test sets has been significantly improved, especially in the CUHK03 test set; the accuracy has increased from 56.4% to 67.5%, an increase of 11.1%. When the two modules are used at the same time, the accuracy rate is higher than that of using either module alone. The reason is that both modules focus on generating more accurate label information, and the information of the two modules is mutually enhanced, making the label allocation in the package more accurate. (2) Expanding the number of weakly annotated categories can significantly improve the accuracy of the model. Divide the training set in dataset CUHK03 into four sets with different numbers of categories to demonstrate the impact of different numbers of categories on model accuracy. Table 3 presents the accuracy levels of Rank-1, 5, and 10. It is evident that as the number of weakly labeled categories increases, the accuracy of model recognition significantly improves. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{
\begin{tabular}{c} **Whether** \\ **to Use** \\ \end{tabular} } & \multicolumn{2}{c}{**Markt-1501**} & \multicolumn{2}{c}{**CUHK03**} \\ \hline  & & mAP & Rank-1 & mAP & Rank-1 \\ \multicolumn{5}{c}{Probability map and} & - & 57.4 & 81.9 & 38.5 & 56.4 \\ multi-feature fusion module & \(\surd\) & 61.1 & 87.2 & 43.3 & 62.3 \\ \multicolumn{5}{c}{Image module + feature} & \(\surd\) & 63.5 & 89.6 & 45.8 & 67.5 \\ \multicolumn{5}{c}{fusion module} & \(\surd\) & 65.4 & 91.1 & 49.8 & 71.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Module effectiveness analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{2}{c}{**CUHK03**} \\ \hline  & Rank-1 & Rank-5 & Rank-10 \\
36 & 12.6 & 35.6 & 59.6 \\
227 & 34.3 & 50.3 & 64.3 \\
467 & 52.6 & 64.5 & 75.0 \\
767 & 71.2 & 80.3 & 88.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data scalability analysis. (3) According to the experimental results, the more weakly labeled categories there are, the more advantageous the accuracy of the model is. However, the more pedestrians there are in a package, the greater the impact on the accuracy of the model. This is because, during model initialization, pseudo-labels need to be assigned to images. If the number of pedestrians increases, the uncertainty of pseudo-label allocation increases, making it more difficult for more images to obtain correct labels. To this end, experiments were conducted on datasets CUHK03 and PRID2011, where the images in the test set were grouped into one package with 1 pedestrian ID (equivalent to fully supervised), 2 pedestrian IDs, 3 pedestrian IDs, 10 pedestrian IDs, and a random number of pedestrian IDs.