# Exploring Gait Recognition in Wild Nighttime Scenes  
[PERSON]  
1 Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China; hotelslee@foxmail. factors to conduct comprehensive studies. These factors include, but are not limited to, viewpoint variations, subjects' attire, and carried items. The development and analysis of diverse datasets play a vital role in advancing gait recognition technology and bridging the gap between laboratory conditions and real-world applications. Several datasets have been created to study these influencing factors. For instance, the CASIA-B [1] dataset incorporates gait sequences under three different walking conditions (normal, wearing a coat, and carrying a bag) from multiple viewpoints. However, early datasets such as CASIA-B and OU-MVLP [2] were collected in indoor laboratory environments, as shown in Figure 1a,b. These idealized scenarios fail to fully reflect the complexities encountered in real-world applications. Consequently, methods based on such indoor, idealized data often exhibit suboptimal performance when applied to outdoor, real-world scenarios [3]. In recent years, researchers have increasingly focused on gait recognition in the wild [3; 4; 5], attempting to investigate gait recognition in more realistic environments. This shift toward real-world scenarios has led to the development of more comprehensive and challenging datasets. For instance, the GREW dataset provides a large-scale benchmark for gait recognition in the wild, with the addition of a distractor set to enhance its applicability to real-world scenarios. The Gait3D dataset, collected in a large supermarket, offers various human representations. Examples of pedestrians from GREW and Gait3D are illustrated in Figure 1c,d, respectively. However, researchers have paid limited attention to variations in lighting conditions in outdoor environments, particularly regarding gait recognition in low-light nighttime scenarios. Under such conditions, the clarity of video captured by cameras significantly deteriorates, posing substantial challenges to existing gait recognition methods. Consequently, improving the accuracy of gait recognition in outdoor low-light conditions has emerged as a pressing issue that demands resolution. Nevertheless, there is currently a lack of suitable datasets that provide gait recognition data in real-world nighttime scenarios. To further explore nighttime gait recognition, we constructed a dataset named Gait Recognition of Day and Night (GaitDN) using videos captured with outdoor public area cameras. GaitDN was collected in outdoor public areas near residential zones and comprises 1009 subjects with over 3000 gait sequences captured in unconstrained wild environments. To facilitate research and comparative analysis, GaitDN also includes gait data collected from the same area during daytime with ample illumination, as illustrated in Figure 2.  
Through the analysis of the collected data, we observed a significant decrease in video clarity under low-light nighttime conditions, as illustrated in Figure 3. This results in blurred boundaries between pedestrians and backgrounds, making it difficult to extract clear human silhouettes from the original images. While silhouette-based methods have  
Figure 1: Examples of existing mainstream datasets. (**a**) CASIA-B. (**b**) OU-MVLP. (**c**) GREW. (**d**) Gait3D. been widely adopted and have shown promising results in indoor conditions [6; 7], they face critical challenges in low-light outdoor environments. Imperfect silhouette extraction due to blurred boundaries leads to a loss of fine-grained details, particularly in low-light conditions. This loss of detail affects critical gait features such as the precise silhouettes of limb movements and subtle body posture changes during the gait cycle.