# Exploring Gait Recognition in Wild Nighttime Scenes
## 2 Related Works
### Gait Recognition Methods  
Based on the distinct features utilized by the models, gait recognition methods can be broadly categorized into two types: appearance-based methods and pose-based methods. Appearance-based methods typically utilize image sequences containing human silhouette information as input, primarily including binary black and white silhouettes obtained through background subtraction algorithms [17] and Gait Energy Images (GEIs) derived from averaging gait silhouettes over a complete cycle [9]. Due to the outstanding performance of deep learning [18] in computer vision tasks [19], and the proven effectiveness of deep convolutional neural networks (CNNs) in extracting image features, CNNs dominate appearance-based methods. For Gait Energy Images, methods such as GEINet [9; 10] construct models based on CNNs to extract effective gait information from GEIs, significantly outperforming previous approaches. Recent methods [3; 20; 21; 22; 23; 24] are mostly based on human silhouettes, exploring the use of different CNNs or multi-scale structures to learn discriminative features directly from silhouette sequences. For instance, GaitSet [24] treats gait sequences as deep sets and uses CNNs to independently extract frame-level features from each silhouette. GaitPart [20] introduces focus convolution, enabling top convolutional kernels to attend to more local details within specific parts of input frames, enhancing the fine-grained learning of part-level spatial features, modeling of local micromotion features, and global understanding of the entire gait sequence. CSTL [21] fuses multi-scale temporal features from local and global perspectives, treating cross-scale contextual information as guidance for temporal aggregation, thereby providing high-quality spatial cues. GaitGL [22] establishes global and local feature extractors and constructs a local temporal aggregation module to replace traditional spatial pooling layers, thereby preserving more spatial information. MTSGait [3] is a 2D convolution-based multi-hop temporal transform method, ensuring that the model can learn both spatial and multi-scale temporal information simultaneously while avoiding issues of excessive model parameters and training difficulties. GLN [23] utilizes the inherent feature pyramid in deep CNNs to enhance gait representation, fusing silhouette-level and set-level features extracted at different stages in a top-down manner with lateral connections to learn discriminative and compact representations from gait silhouettes. Although these methods have significantly improved performance on indoor datasets, they are susceptible to factors such as clothing, camera angle, walking speed variations, and lighting changes, resulting in poor robustness and transferability. Pose-based methods utilize human skeleton sequences extracted from raw images as input, learning intrinsic correlations between body skeletons to extract gait features, often demonstrating stronger robustness. Posegait [25] uses 2D skeletons extracted from RGB images to estimate 3D pose information [26], combining a CNN with LSTM to design a dedicated gait feature extractor for 3D pose information. Given the success of graph convolutional neural networks in action recognition, graph convolution-related methods have gradually been introduced into gait recognition. GaitGraph [27] uses HR-Net [28] to extract 2D human skeleton points and applies a ResGCN module architecture network to learn gait features. GPCAit [29] proposes a series of human-centric preprocessing operations to overcome environmental covariance influences, establish unified human pose representations, and achieve efficient graph partitioning and local-global feature relationship extraction. Furthermore, with the popularity of Transformers and their ability to capture long-range dependencies effectively, [PERSON] et al. proposed a novel heterogeneous spatiotemporal axial mixer, GaitMixer [30], that can efficiently learn discriminative gait representations by capturing high-frequency and low-frequency features.