# Exploring Gait Recognition in Wild Nighttime Scenes
## 4 The GaitDN Dataset  
To facilitate research on gait recognition in nighttime scenarios, which are prevalent in practical applications, we compiled a compact gait dataset named Gait Recognition of Day and Night (GaitDN). This dataset represents the first wild gait dataset that concurrently encompasses both daytime and nighttime data. The GaitDN dataset comprises 1009 subjects, over 3000 sequences, and more than 100,000 pedestrian images. Specifically, it includes 762 subjects collected during daytime and 247 subjects collected at night. GaitDN was collected in public areas where individuals typically carry various items and walk in irregular patterns and speeds. This characteristic not only incorporates variations in lighting conditions but also includes diverse interference factors, more closely approximating real-world gait recognition scenarios. The dataset thus provides a more authentic representation of the challenges encountered in practical applications. A comparison between GaitDN and other mainstream datasets is shown in Table 1.  
The construction of the GaitDN dataset primarily involves four steps: video collection, pedestrian detection, pedestrian clustering, and feature extraction. To collect gait data from real-world outdoor scenarios, we utilized three cameras installed near public area entrances to gather over 100 h of video data with a resolution of 1920 \(\times\) 1080 and a frame rate of 25 fps. For each frame of the original video, we employed a YOLOv3-based [33] pedestrian detection model to segment and extract pedestrian images, chosen for its speed and accuracy. This process yielded 3300 pedestrian gait sequences, each containing 25 to 60 frames. To cluster sequences of the same individual, we extracted pedestrian re-identification features using the open source FastReID [34] framework and applied unsupervised clustering methods. However, for nighttime data with reduced image quality, these methods proved inadequate for accurate identity classification. Consequently, we employed human annotators to assist in classifying this portion of the nighttime data. For the clustered pedestrian images, we used a pre-trained HRNet [28] model to estimate 2D skeleton data for each image. HRNet is currently the most widely adopted human pose extraction method, offering high accuracy and robust performance in challenging conditions. To ensure that the collected data closely reflect real-world application scenarios, we intentionally did not control any variables other than clearly distinguishing between daytime and nighttime data, as we aimed to gather data that are sufficiently complex to encompass a wide range of variations. Consequently, GaitDN not only incorporates lighting variations but also includes multiple potential factors that may impact recognition accuracy, comprising cluttered backgrounds, environmental occlusions of pedestrians, individuals carrying bags, and pedestrians wearing heavy coats, as illustrated in Figure 2. Some extreme cases of pedestrian gait sequences are shown in Figure 7. For pedestrian a, the two gait sequences exhibit significant variations in lighting direction and color, with clothing colors blending closely with the background, which poses a challenge for feature extraction algorithms. For pedestrian b, the sequences show not only changes in lighting color and intensity but also significant differences in clarity and camera angles. For pedestrian c, the background includes intense lighting variations and moving vehicles, leading to dynamic background changes. These examples highlight the complexity of the data in GaitDN, effectively simulating the challenging scenarios of real-world nighttime environments. To protect privacy, the faces of subjects in the samples were pixelated. The diversity of influencing factors enables GaitDN to better simulate various complex situations that may be encountered in practical applications, providing researchers with a more comprehensive and authentic testing platform.