# Rethink Motion Information for Occluded Person Re-Identification
## 4 Experiments
### Ablation Studies  
**Experiments on motion consistency task. ** When we delete the motion consistency task from the motion-aware transformer, we directly utilize the learnable queries in the motion-aware transformer to extract human part features. As we can see in Table 6 #1, #2, #7, and #8, our approach appears to perform poorly on Occluded ReID tasks and yield similar results on holistic ReID tasks. It is evident that, without the motion consistency task, our method pays more attention to modeling the pedestrian appearance features, which will be greatly influenced by any occlusion. Additionally, unlike previous motion-based methods [21; 23] that directly used motion information, such as [24], in an explicit way, our method first obtains implicit motion information through a motion-aware transformer and a motion consistency task. Then, a fusion encoder is utilized to fuse the visual features and the implicit motion tokens in an \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Occluded-REID**} \\  & **Rank-1** & **mAP** \\ \hline FPR [44] & 78.3 & 68.0 \\ PCB [45] & 41.3 & 38.9 \\ AMC+SWM [36] & 31.2 & 27.3 \\ PVPM [51] & 70.4 & 61.2 \\ HOReID [48] & 80.3 & 70.2 \\ PAT [7] & 81.6 & 72.1 \\ TransReID [50] & 70.2 & 67.3 \\ DPM [6] & 85.5 & 79.7 \\ \hline
**Ours** & **86.7** & **81.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with state-of-the-art methods on Occluded-REID dataset. Our method achieves the best performance. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Market-1501**} \\  & **Rank-1** & **mAP** \\ \hline FPR [44] & 95.4 & 86.6 \\ PCB [45] & 92.3 & 77.4 \\ PGFA [46] & 91.2 & 76.8 \\ VPM [47] & 93.0 & 80.8 \\ HOReID [48] & 94.2 & 84.9 \\ ISP [49] & 95.3 & 88.6 \\ PAT [7] & 95.4 & 88.0 \\ TransReID [50] & 95.0 & 88.2 \\ FED [8] & 95.0 & 86.3 \\ DPM [6] & 95.5 & 89.7 \\ \hline
**Ours** & **95.7** & **89.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with state-of-the-art methods on Market-1501. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Partial-REID**} & \multicolumn{2}{c}{**Partial-iLIDS**} \\  & **Rank-1** & **Rank-3** & **Rank-1** & **Rank-3** \\ \hline FPR [44] & 81.0 & - & 68.1 & - \\ AMC+SWM [36] & 37.3 & 46.0 & 21.0 & 32.8 \\ PVPM [51] & 78.3 & 87.7 & - & - \\ PGFA [46] & 68.0 & 80.0 & 69.1 & 80.9 \\ VPM [47] & 67.7 & 81.9 & 65.5 & 74.8 \\ HOeID [48] & 85.3 & 91.0 & 72.6 & 86.4 \\ PAT [7] & 88.0 & 92.3 & 76.5 & 88.2 \\ FED [8] & 84.6 & - & - & - \\ \hline
**Ours** & **89.2** & **93.2** & **77.5** & **89.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison with state-of-the-art methods on Partial REID dataset, and Partial-iLIDS dataset. Our method achieves the best performance on two datasets. explicit way. Here, we have demonstrated that the introduction of motion information is the main factor in dealing with occluded situations. **Experiments on visual adapter and motion-aware transformer. ** To provide a feeling of the importance of the visual adapter and the motion-aware transformer, we add two ablation experiments and summarize results in Table 6. As shown in Table 6 #3, our methods can only handle video-based-related ReID tasks when the visual adapter is introduced. As shown in Table 6 #6 and #8, removing VA affects the accuracy of video-based tasks (MARS) (1.3% drop) more than other image-based tasks. This is because the learnable latent queries of the VA module can aggregate and learn spatio-temporal information for multiple video frames, while this has no effect on still images. The MAT module utilizes motion information to focus more on human part segments. As shown in Table 6 #5 and #8, the absence of MAT largely reduces the accuracy of O-REID (3.2% drop) and P-REID (6.1% drop) as the attention of human-part segmentation is affected by background noises. **Experiments on the length of learnable queries in visual adapter.