# Exploring Gait Recognition in Wild Nighttime Scenes
## 3 GaitSAT Framework
### Multi-Scale Temporal Rhythm Perception Module  
Human gait, as a process with periodic patterns, inherently encompasses regular changes across multiple temporal scales. These rhythmic variations, while generally consistent for an individual, can differ significantly among people due to variations in walking speed. The diversity in walking speeds leads to distinctive temporal patterns in gait cycles. In real-world scenarios, pedestrian walking speeds vary considerably, as illustrated in Figure 5, further emphasizing the need to account for diverse temporal patterns. Existing temporal convolution approaches with a fixed time-step [8; 11], when processing these variations, often extract features only at specific time intervals, limiting the ability to accommodate the diverse walking speeds and patterns exhibited in real-world scenarios. Specifically, convolution kernels with fixed strides can only slide over a particular time stride, leading to the loss of critical temporal information and comprehensive gait patterns.  
To address this issue, we propose a multi-scale temporal rhythm perception module, as illustrated in Figure 6. This module comprises three parallel branches designed to capture gait patterns at different temporal granularities: the fine temporal branch (temporal stride of 1), the middle-scale temporal branch (temporal stride of 2), and the coarse temporal branch (temporal stride of 3), which significantly outperforms fixed-stride models in several aspects: (1) The multi-scale structure enables comprehensive feature extraction across different temporal resolutions, preventing the loss of critical temporal information that often occurs with fixed-stride approaches. (2) The parallel processing of different temporal scales allows the model to adaptively capture both rapid, subtle movements and slower, more sustained gait patterns simultaneously. (3) This architecture inherently enhances the model's robustness to variations in walking speed and irregular gait patterns, as it can effectively process temporal information at multiple scales concurrently. (4) The combination of different temporal scales creates a more complete representation of gait dynamics, leading to more reliable and accurate recognition results in challenging real-world scenarios. In each branch, we introduce Self-Attention Temporal Fusion Units (SAT Units) as the basic modules. Each SAT Unit consists of an SA-GC block and a Multi-Scale Temporal Convolutional Network (MS-TCN) [32] block connected in series. The MS-TCN includes three convolution branches with different combinations of kernel sizes and dilation rates, each used to learn temporal features of varying granularity. Additionally, the MS-TCN employs \(1\times 1\) convolutions as residual modules, as shown in Figure 4b. We construct each branch's structure by stacking multiple SAT Units with the same time stride, where the number of SAT Units can be dynamically adjusted based on the characteristics of different datasets. For instance, for the Gait3D dataset, we stack three SAT Units in each branch. Our approach possesses inherent dynamic adaptability, allowing for the flexible adjustment of the number of SAT Units according to task requirements. This dynamic structure is not only simple but it also enables the model to adaptively handle gait recognition tasks of varying complexity. While maintaining a consistent core architecture, it achieves the efficient processing of datasets of various scales and characteristics, significantly enhancing its flexibility and efficiency in handling diverse gait data.