# Rethink Motion Information for Occluded Person Re-Identification
## 4 Experiments
### Comparison with State-of-the-Art Methods  
**Comparisons on Video-based Datasets.** On account of the design of the visual adapter, our method could deal with video-based ReID tasks. As shown in Table 2, when comparing our approach with state-of-the-art methods, we have achieved comparable performance. Especially on the MARS and LS-VID datasets, we have achieved the highest Rank-1 and mAP. The results of the video-based methods have demonstrated that our visual adapter enables us to distill the spatio-temporal features and the dependencies among video frame features.  
**Comparisons on Holistic Datasets.** The results on the Market-1501 are shown in Table 3. It is clear that our method achieves the best performance compared to other state-of-the-art methods and with other part-based or global-based methods. This demonstrates that our approach could obtain more representative features for holistic pedestrians.  
**Comparisons on Occlusion Datasets.** The results on the Occluded-REID are shown in Table 4 and the results on the Partial datasets are shown in Table 5. When comparing our method with state-of-the-art methods, we achieve the highest Rank-1 and mAP on both Occluded-REID and Partial datasets. Especially on the partial datasets, we achieve 89.2%/93.2% and 77.5%/89.6% on Rank-1/Rank-3 for Partial-REID and Partial-iLIDS, respectively.  
As we all know, occluded ReID tasks always need more refined part features to perform pedestrian retrieval. Through these experiments, our method has demonstrated a strong ability to cover occlusions. The reason could be summed up in two ways: On the one hand, the motion consistency task guides the model to extract human part features and aggregate the motion information using learnable queries. On the other hand, the fusion  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MARS**} & \multicolumn{2}{c}{**LS-VID**} & \multicolumn{2}{c}{**iLIDS-VID**} & \multicolumn{2}{c}{**PRID-2011**} \\  & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **Rank-5** & **Rank-1** & **Rank-5** \\ \hline M3D [9] & 84.4 & 74.1 & 57.7 & 40.1 & 74.0 & 94.3 & 94.4 & **100.0** \\ AP3D [10] & 90.1 & 85.1 & 84.5 & 73.2 & 88.7 & - & - & - \\ CRL [42] & 91.0 & 84.8 & - & - & 90.4 & **98.3** & 96.2 & 99.7 \\ GLTR [39] & 87.0 & 78.5 & 63.1 & 44.3 & 86.0 & 98.0 & 95.5 & **100.0** \\ MGH [11] & 90.0 & 85.8 & - & - & 85.6 & 97.1 & 94.8 & 99.3 \\ MG-FAFA [43] & 88.8 & 85.9 & - & - & 88.6 & 98.0 & 95.9 & 99.7 \\ STMN [14] & 90.5 & 84.5 & 82.1 & 69.2 & 91.5 & - & - & - \\ CAViT [13] & 90.8 & 87.2 & 89.2 & 79.2 & 93.3 & 98.0 & 95.5 & 98.9 \\ SBM [12] & 91.0 & 86.2 & 87.4 & 79.6 & **92.5** & - & **96.5** & - \\ \hline
**Ours** & **91.1** & **86.4** & **87.6** & **79.9** & 92.4 & 97.9 & 96.4 & **100.0** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison with state-of-the-art methods on MARS, LS-VID, iLIDS-VID, and PRID-2011 datasets. Our method achieves a competitive performance on four datasets.  
encoder combines the motion information with original vision features, which helps the model generate discriminative representations.