# Exploring Gait Recognition in Wild Nighttime Scenes
Consequently, the degradation of these key characteristics significantly compromises the effectiveness of silhouette-based methods, which rely heavily on accurate and detailed body silhouettes for recognition. The aforementioned limitations highlight the need for alternative approaches that can maintain robust performance across various lighting conditions. To address this challenge, we propose a pose-based gait recognition framework, GaitSAT. Utilizing human pose information, which can be more reliably extracted in low-light conditions compared to silhouettes, GaitSAT maintains consistent performance across diverse lighting scenarios, solving the problem of performance degradation in challenging nighttime environments that plague silhouette-based methods. By focusing on body joints and their relationships, GaitSAT aims to capture essential gait characteristics even when detailed silhouettes are not discernible. The algorithm combines graph convolutional neural networks with self-attention mechanisms to effectively learn and analyze structural changes in the human body during walking, which can maximally mitigate the effects of lighting variations. Furthermore, considering that human walking is a process with periodic patterns, extracting effective temporal features is crucial. Many existing methods, such as those using Gait Energy Images (GEIs) or temporal convolutional networks [8], have inherent mechanisms to handle variations in walking speed to some extent. For instance, GEI-based [9; 10] approaches implicitly average out speed variations over a gait cycle, while temporal convolutions can capture patterns across different time scales. However, in complex outdoor scenarios, pedestrians exhibit a wide range of walking speeds, which  
Figure 2: Examples of GaitDN dataset (with key information obscured). (**a**) Daytime data. (**b**) Nighttime data. poses significant challenges to existing methods. Although current temporal convolution approaches [8,11] have considered the importance of temporal features, they lack the adaptability required for more complex cases of speed variation, potentially leading to a loss in critical temporal information. To enhance the model's robustness and accuracy in these scenarios, we introduced a multi-scale temporal rhythm perception module. By integrating temporal information from different time steps, the module enhances the model's ability to handle diverse speed variations and walking patterns, improving recognition accuracy in complex outdoor environments. This adaptation significantly boosts the model's overall performance, providing a more comprehensive and effective approach to gait recognition in real-world settings. In summary, the contributions of this paper are as follows:  
* To address the limitations in existing research on nighttime gait recognition, we constructed the first outdoor gait dataset that encompasses both daytime and nighttime data, named Gait Recognition of Day and Night (GaitDN). This dataset provides data support for gait recognition research and applications, particularly in low-light nighttime scenarios. * A novel gait recognition framework GaitSAT is introduced, which is particularly suited for processing gait recognition tasks in complex nighttime scenarios. GaitSAT not only enhances the model's adaptability to low-light outdoor scenes but also provides a flexible and generalizable framework for future research. * Experimental results demonstrate that GaitSAT achieves state-of-the-art recognition accuracy and generalization performance among existing skeleton-based gait recognition methods in nighttime outdoor scenarios. These findings substantiate the effective  
Figure 3: Collected daytime and nighttime data with corresponding human silhouettes. (**a,b**) show data collected during the daytime, where clear human silhouettes can be successfully extracted. (**c,d**) show data collected during the nighttime, where the silhouettes are either unidentifiable or incomplete.