# Exploring Gait Recognition in Wild Nighttime Scenes
## 2 Related Works  
### Gait Recognition Datasets  
Gait recognition datasets have evolved significantly, reflecting the field's progression from controlled laboratory scenarios to more complex, real-world scenarios. This evolution can be broadly categorized into three stages: early controlled environments, transition to outdoor scenarios, and recent efforts to capture real-world complexity.  
In the initial stage, researchers focused on isolating specific factors affecting gait recognition. Datasets like the CASIA series [1,12] and OU-ISIR series [2,13,14] were instrumental in this phase. The CASIA-B dataset, for instance, investigates walking states and viewpoint variations, offering three distinct conditions (normal walking, carrying a bag, and wearing a coat) from 11 viewpoints. Similarly, the OU-ISIR Speed [13] dataset explores the impact of walking speed, while the OU-ISIR LP [2] Bag dataset addresses the challenge of recognizing individuals carrying objects. These datasets, collected in controlled indoor environments, allowed researchers to systematically study individual factors influencing gait recognition.  
However, the controlled nature of these datasets created a significant gap between research and real-world applications. Recognizing this limitation, the field began transitioning toward more realistic scenarios. This shift is exemplified by datasets like GREW and Gait3D. The GREW dataset provides a large-scale benchmark for gait recognition in the wild, encompassing 26,345 pedestrians and 128,671 gait sequences, with the addition of a distractor set to enhance its applicability to real-world scenarios. The Gait3D dataset, collected in a large supermarket, comprises 4000 pedestrians and 25,309 gait sequences, offering various human representations including silhouettes, 2D/3D skeletons, 3D meshes, and 3D SMPL data. Recent efforts have focused on capturing even more nuanced aspects of real-world gait recognition. The CCPG [5] dataset, for example, addresses the challenge of clothing changes, offering refined conditions including overall fit changes, top and bottom changes, and carrying bags. This progression toward more complex and varied datasets reflects the field's growing emphasis on developing robust recognition algorithms capable of performing in diverse, real-world conditions.  
Despite these advancements, a significant gap remains in the realm of nighttime gait recognition [15,16]. The challenges posed by low-light conditions in outdoor environments have not been adequately addressed by existing datasets. The CASIA Infrared [12] dataset, while pioneering in nighttime gait recognition, lacks the viewpoint variations and environmental complexities characteristic of real-world scenarios. This gap in available data highlights the need for a comprehensive dataset that captures the unique challenges of nighttime gait recognition in outdoor settings. Such a dataset would need to account for factors like varying lighting conditions, complex backgrounds, and the potential for irregular walking patterns exacerbated by low visibility. Our research aims to address this crucial need by developing a gait dataset that richly captures these nighttime variations.