# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion
### Performance Comparison with State-of-the-Art Methods  
Our method uses human poses as input and belongs to the pose-based approach category. Therefore, we selected three mainstream pose-based gait recognition methods for comparison with our proposed method. GaitGraph, designed based on gait data from ideal environments, does not perform well on outdoor gait datasets. While this method achieves higher accuracy on indoor datasets compared to other pose-based methods, its performance on outdoor datasets does not reach the same level as on indoor datasets. GPGait demonstrates robust generalization capabilities and performs excellently on outdoor datasets. The selected methods are briefly introduced as follows:  
1. GaitGraph is a representative pose-based method that first introduced 2D human poses into gait recognition. It employs graph convolutional networks [38] for spatiotemporal modeling and uses supervised contrastive loss for training. 2. GaitTR pioneers the incorporation of spatial Transformers in gait recognition, combining them with temporal convolutional networks to extract gait features from skeletons. 3. GPGait proposes a series of human-oriented methods for preprocessing human poses, aiming to obtain unified and rich representations. It also utilizes mask design to achieve effective graph partitioning and extraction of local-global feature relationships. The experimental results of existing pose-based methods and our GaitSAT on various datasets are presented in Table 6. GaitSAT demonstrated outstanding performance on outdoor datasets, achieving Rank-1 accuracies of 22.90% and 57.37% on the outdoor datasets Gait3D and GREW, respectively, representing the current state-of-the-art performance. For our proposed outdoor dataset GaitDN, which includes nighttime data, GaitSAT achieved a Rank-1 accuracy of 86.24%, also the best performance to date. These results indicate that GaitSAT is not only suitable for outdoor scenarios but also performs well in low-light nighttime conditions, maintaining excellent performance when facing complex recognition tasks. For indoor datasets, GaitSAT's performance on the smaller-scale CASIA-B was not superior to existing methods but still demonstrated stable performance. Moreover, on OU-MVLP, currently the largest indoor dataset, GaitSAT achieved state-of-the-art performance with a \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Block**} & \multicolumn{2}{c}{**Setting**} & \multirow{2}{*}{**CASIA-B**} & \multirow{2}{*}{**Gait3D**} & \multirow{2}{*}{**Gait3D**} \\ \cline{2-3} \cline{5-6}  & **Stride** & & & \\ \hline \multirow{5}{*}{TCN [8]} & All stride = 1 & 58.32 & 13.65 & 63.67 \\ \cline{2-6}  & All stride = 2 & 56.33 & 13.98 & 62.69 \\ \cline{2-6}  & All stride = 3 & 55.97 & 12.11 & 58.98 \\ \cline{2-6}  & stride = 1, 2, 3 & 59.86 & 14.92 & 54.10 \\ \hline \multirow{5}{*}{Temporal Bottleneck Block [11]} & All stride = 1 & 69.20 & 17.53 & 77.38 \\ \cline{2-6}  & All stride = 2 & 67.56 & 17.04 & 76.52 \\ \cline{2-6}  & All stride = 3 & 63.05 & 15.54 & 74.81 \\ \cline{2-6}  & stride = 1, 2, 3 & 71.97 & 18.67 & 78.96 \\ \hline \multirow{5}{*}{MS-TCN} & All stride = 1 & 73.74 & 22.60 & 85.19 \\ \cline{2-6}  & All stride = 2 & 67.56 & 17.04 & 76.52 \\ \cline{1-1} \cline{2-6}  & All stride = 3 & 72.41 & 20.90 & 73.77 \\ \cline{1-1} \cline{2-6}  & stride = 1, 2, 3 & **76.32** & **22.90** & **86.24** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of different temporal aggregation methods using proposed multi-scale temporal rhythm perception module. Rank-1 accuracy of 60.77%. GaitSAT's excellent performance across multiple large-scale datasets proves its superiority in processing large-scale data. These results demonstrate that GaitSAT possesses outstanding adaptability in various environments, particularly excelling in outdoor and complex scenarios, showing broad potential for practical applications. Through a comprehensive analysis of computational resources, we thoroughly evaluated the scalability of GaitSAT.