# Exploring Gait Recognition in Wild Nighttime Scenes
## 6 Conclusions  
In this work, to explore more effective gait recognition in the wild, we collected GaitDN, the first gait dataset explicitly distinguishing between daytime and nighttime data, and proposed GaitSAT, a method more adaptable to outdoor scenarios. GaitSAT employs attention-based graph convolution to model the intrinsic connections between human skeleton joints and uses a multi-scale temporal rhythm perception module to aggregate temporal features, ultimately obtaining rich human gait representations. GaitSAT not only outperforms baseline models on public datasets but also further validates its excellent cross-domain performance and robustness in cross-domain tasks. Additionally, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Source Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Target DataSet**} \\ \cline{3-4}  & & **GaitDN\_Day** & **GaitDN\_Night** \\ \hline \multirow{4}{*}{CASIA-B} & GaitGraph & 21.56 & 6.52 \\ \cline{2-4}  & GaitTR & 21.16 & 18.33 \\ \cline{2-4}  & GPGait & **81.48** & 61.67 \\ \cline{2-4}  & GaitSAT & 78.84 & **61.96** \\ \hline \multirow{4}{*}{OUMVLP} & GaitGraph & 40.52 & 26.08 \\ \cline{2-4}  & GaitTR & 55.55 & 40.10 \\ \cline{2-4}  & GPGait & **80.95** & 56.37 \\ \cline{2-4}  & GaitSAT & 77.78 & **56.67** \\ \hline \multirow{4}{*}{GREW} & GaitGraph & 84.97 & 69.56 \\ \cline{2-4}  & GaitTR & 64.55 & 53.33 \\ \cline{2-4}  & GPGait & 87.83 & 71.56 \\ \cline{2-4}  & GaitSAT & **88.89** & **71.67** \\ \hline \multirow{4}{*}{Gait3D} & GaitGraph & 65.39 & 43.48 \\ \cline{2-4}  & GaitTR & 40.74 & 28.33 \\ \cline{2-4}  & GPGait & 85.56 & 69.89 \\ \cline{2-4}  & GaitSAT & **86.78** & **70.00** \\ \hline \multirow{4}{*}{GaitDN} & GaitGraph & 72.19 & 65.36 \\ \cline{2-4}  & GaitTR & 62.93 & 57.44 \\ \cline{1-1} \cline{2-4}  & GPGait & **87.83** & 66.07 \\ \cline{1-1} \cline{2-4}  & GaitSAT & 86.17 & **68.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross-Domain performance comparison between GaitSAT and state-of-the-art methods on daytime and nighttime data from GaitDN. The source datasets are mainstream datasets, and the target datasets are the daytime and nighttime data from GaitDN, respectively. ablation experiments demonstrate the crucial role of each GaitSAT component in enhancing model performance. Future work could explore several potential directions. Firstly, improving the quality of original images is crucial for gait recognition in complex scenarios. Besides upgrading camera equipment, effective data preprocessing methods could be explored to enhance image clarity by adjusting parameters such as contrast and brightness while minimizing interference with original human information, thus providing higher-quality input for silhouette-based methods. Secondly, using multi-modal approaches [39; 40; 41; 42] to simultaneously learn gait representations from silhouette and skeleton information is a promising research direction. How to effectively combine silhouette and skeleton information merits further exploration by researchers. Lastly, we are committed to building a comprehensive gait recognition platform. In future work, we plan to introduce additional factors that may influence gait recognition, such as terrain and weather conditions. Our ultimate goal is to develop gait recognition models with greater robustness and scalability, thereby advancing the practical applications of gait recognition. Furthermore, the relevant data in this paper contain privacy information of the subjects, and we will make every effort to protect their privacy. Firstly, all collected data are for research purposes only, and any original videos or RGB images related to the subjects will not be made public. The samples shown in this paper had their faces pixelated. Secondly, processed data, such as 2D skeleton data, will only be released after strict approval from the relevant management level, and researchers will need to sign a rigorous application form before using the data for academic research. Conceptualization, H.L. ; methodology, H.L.