## 3 Model and Methods
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**MARS**} & \multicolumn{4}{c}{**iLIDS-VID**} & \multicolumn{2}{c}{**PRID-2011**} \\ \cline{2-9}  & & **mAP** & **Rank-1** & **Rank-5** & **Rank-1** & **Rank-5** & **Rank-1** & **Rank-5** \\ \hline \multirow{2}{*}{Attribute} & ADFD & 78.2 & 87.0 & 95.4 & 86.3 & 97.4 & 93.9 & 99.5 \\  & AMEM & 79.3 & 86.7 & 94.0 & 87.2 & 97.7 & 93.3 & 98.7 \\ \hline \multirow{3}{*}{Graph} & MGH & 85.8 & 90.0 & 96.7 & 85.6 & 97.1 & 94.8 & 99.3 \\  & STGCN & 83.7 & 89.9 & 96.4 & - & - & - & - \\  & CTL & 86.7 & 91.4 & 96.8 & 89.2 & 97.0 & - & - \\ \hline \multirow{3}{*}{3D convolution} & AP3D & 85.6 & 90.7 & - & 88.7 & - & - & - \\  & STRF & 86.1 & 90.3 & - & 89.3 & - & - & - \\  & SSN3D & 86.2 & 90.1 & 96.6 & 88.9 & 97.3 & - & - \\ \hline \multirow{3}{*}{Attention} & DenseIL & 87.0 & 90.8 & 97.1 & 92.0 & 98.0 & - & - \\  & STT & 86.3 & 88.7 & - & - & - & - & - \\  & SINet & 86.2 & 91.0 & - & 92.5 & - & 96.5 & - \\ \hline \multirow{3}{*}{Grained} & MG-RAFA & 85.9 & 88.8 & 97.0 & 88.6 & 98.0 & 95.9 & 99.7 \\  & GRL & 84.8 & 91.0 & 96.7 & 90.4 & 98.3 & 96.2 & 99.7 \\  & PiT & 86.8 & 90.2 & 97.2 & 92.1 & 98.9 & - & - \\ \hline \multicolumn{3}{c}{APMG networks} & 88.4 & 92.1 & 97.8 & 93.6 & 98.7 & 97.1 & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The re-identification accuracy of each model. points in a GCN structure are represented by coarsely grained features, a GCN network does not make full use of the detailed distinguishable feature information in an image. In contrast, our APMG network makes use of various features with different levels of granularity and also pays attention to the importance of features in time. Furthermore, the APMG network's transformer structure and CAP module allows the network to learn the global features of an image without losing the original information. For these reasons, the architecture and performance of the APMG network are superior to that of GCN-based networks. When compared with attention-based methods, the transformer in the APMG network has the structural characteristics of the global receptive field, which can extract enough feature information from each frame. It is not necessary to expand the attention area in the current image through the previous frames as SINet does. When compared with attribute-based methods, the APMG network's mAP score in the MARS dataset increased by 9.1%. The available attribute information in person images mainly depends on the extraction of the network, it may have a large error due to the change in visual angle. However, the APMG network mainly extracts the multi-granularity information of people, and through the multi-granularity information fusion between frames, it can effectively reduce the influence of the visual angle on the network performance. In summary, the APMG network has achieved the most advanced performance. To verify the effectiveness of the APMG network, we randomly select three video sequences from the MARS dataset and examine the activation visualisation results for these sequences, as shown in Figure 5. The first group of images in the figure shows that the APMG network effectively extracts distinguishable feature information of people, such as a ponytail, a schoolbag, an arm, and other feature information. Figure 5 also shows that the APMG network can extract person-feature information from an image with a cluttered background (such as in the third image in the first group) or when the person is blocked (such as in the second image in the second group).