# Rethink Motion Information for Occluded Person Re-Identification
## 4 Experiments
### Visualization  
**Overview. ** Our proposed framework for ReID is based on motion information, and after sufficient training, the network will focus its attention on pedestrians, as shown in Figure 4a. Figure 4b also shows the effect of the motion consistency task that we have introduced, whose main purpose is to learn the features of each part of the human body; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Length** & **Market-1501** & **O-REID** & **P-REID** & **MARS** \\  & **Rank-1** & **Rank-1** & **Rank-1** & **Rank-1** \\ \hline
2 & 94.0 & 84.0 & 83.7 & 89.9 \\
4 & 94.4 & 84.3 & 84.2 & 90.1 \\
6 & 95.0 & 85.7 & 86.3 & 90.4 \\
8 & 95.2 & 86.3 & 88.9 & 90.9 \\
10 & **95.7** & **86.7** & **89.2** & **91.1** \\
16 & 95.4 & 85.2 & 88.1 & 90.9 \\
32 & 95.0 & 84.1 & 84.0 & 90.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The experiments about the length of learnable queries in motion-aware transformer on Market-1501, Occluded-REID, Partial-REID, and MARS. Here, O-REID is the abbreviation of Occluded-REID and P-REID is the abbreviation of Partial-REID. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**O-REID**} & \multicolumn{2}{c}{**P-REID**} \\  & **Rank-1** & **mAP** & **Rank-1** & **Rank-3** \\ \hline w & **86.7** & **81.2** & **89.2** & **93.2** \\ w/o & 86.1 \((-0.6)\) & 79.8 \((-0.4)\) & 88.5 \((-0.7)\) & 92.7 \((-0.5)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The experiments on either pre-trained on video datasets. Here, O-REID is the abbreviation of the Occluded-REID and P-REID is the abbreviation of Partial-REID. The gray represents the value affected. this whole process is similar to a semantic segmentation process, and the occlusions, such as cars, plates, will be considered as background noise for the motion consistency task. As shown in Figure 5a, we show the image-based and video-based attention maps from the visual adapter module which sense the approximate range of the human body and suppress the background noise. As shown in Figure 5b, the latent token introduced in the motion consistency task can be found through its attention graph that the latent token pays more attention to the detailed features of each part of the human body, and at the same time, it has a certain suppression effect on the background noise. Therefore, the method proposed in this paper can effectively reduce the background noise caused by objects that may change their positions, such as cars and license plates. **The visualization of human part segmentation. ** We visualize the human part segmentation from the motion consistency task in Figure 4. In Figure 4a, we can intuitively find that after adding motion information, the attention map is more focused on the human body and has a strong resistance to occlusions. In Figure 4b, when the pedestrian rides a bike in various postures or is occluded by a car, the model is still able to effectively distinguish between human and obscured. It is worth noting that all of these enhancements are made possible by introducing motion information. **The visualization of attention map and the CMC curve. ** Taking one still image or several video frames as input, the visual adapter module will first extract coarse global features (Figure 5a) of the approximate motion area. Then, the MAT module further learns the refined motion information for each part of the human body from the coarse global features and provides more distinguishable human part features (Figure 5b). As shown in Figure 6, we demonstrate that introducing the motion information can reduce the sensitivity to human appearance. At last, as shown in Figure 7, we provide the CMC curve for every datasets. Figure 4: **(a)** We show the attention map of MAF, and it can be found that the attention of MAF is more focused on the human body region part and less affected by the background noise.