TLDR: A new framework called GaitRDAE improves gait recognition by dynamically analyzing human walking patterns. It automatically identifies motion-rich body regions (like hands and feet) and assigns them adaptive temporal scales for better understanding. Two core modules, Region-aware Dynamic Aggregation (RDA) and Region-aware Dynamic Excitation (RDE), work together to focus on stable motion patterns while suppressing static, changeable features. This leads to state-of-the-art performance on various datasets, making gait recognition more robust against factors like clothing and camera angles.
Gait recognition, the science of identifying individuals by their unique walking patterns, holds immense potential for applications ranging from video surveillance to security checks. Unlike facial recognition, gait recognition is less affected by changes in clothing or appearance, as it focuses on the inherent motion patterns of a person. However, current deep learning methods for gait recognition often struggle with the dynamic and diverse nature of human movement, especially when factors like different clothing or camera angles (covariates) alter visual appearance.
A significant challenge for existing gait recognition systems is their reliance on predefined regions and fixed temporal scales for analyzing movement. This means they treat all parts of the body and all types of motion with the same analytical lens, which isn’t ideal. Motion-rich areas, like hands and feet, exhibit complex, rapidly changing patterns that require a longer look, or ‘temporal receptive field,’ to understand fully. Static regions, such as the torso, have more consistent patterns and need less extensive temporal analysis. When models fail to adapt to these differences, they become overly sensitive to static changes (like clothing variations) and miss the crucial, stable motion patterns.
To address these limitations, researchers have introduced a novel framework called GaitRDAE: Region-aware Dynamic Aggregation and Excitation. This innovative approach automatically identifies motion regions, assigns them adaptive temporal scales, and applies tailored attention to enhance recognition accuracy. The core of GaitRDAE lies in two key modules: the Region-aware Dynamic Aggregation (RDA) module and the Region-aware Dynamic Excitation (RDE) module.
The
Region-aware Dynamic Aggregation (RDA)
module is designed to dynamically determine the optimal temporal receptive field for each specific body region. Imagine a camera observing a person walking; the RDA module intelligently decides how long to ‘watch’ each part of the body to capture its unique movement characteristics. For instance, it might allocate a longer observation window to a swinging arm or a striding leg, which are rich in dynamic information, while assigning a shorter window to the relatively static torso. This adaptive temporal scaling allows the model to better understand the diverse behavioral patterns across different motion regions.
Complementing RDA, the
Region-aware Dynamic Excitation (RDE)
module focuses on emphasizing the learning of motion regions that contain more stable and discriminative behavior patterns, while simultaneously suppressing attention to static regions that are easily influenced by external factors like clothing or viewpoint changes. The RDE module achieves this through two sub-modules: Spatial-wise Motion Excitation (SME) and Channel-wise Motion Excitation (CME).
The
Spatial-wise Motion Excitation (SME)
module assigns higher spatial attention to motion regions by analyzing the differences in response between adjacent frames. Essentially, if a part of the body shows a lot of change between frames, it’s likely a motion region, and SME gives it more focus. The Also Read:
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Channel-wise Motion Excitation (CME)
module, on the other hand, enhances the channels within the neural network that are particularly sensitive to motion. It focuses on capturing high-frequency variations, which are indicative of dynamic movement, and filters out static information.
The GaitRDAE framework processes gait sequences through multiple stages, strategically placing the RDA and RDE modules to progressively learn dynamic spatio-temporal characteristics. This ensures that the model first aggregates spatial features, then enhances local spatial information, applies attention to global motion features, and finally aggregates temporal features, leading to a comprehensive extraction of motion patterns.
Extensive experiments were conducted on several benchmark datasets, including both ‘in-the-wild’ scenarios (GREW and Gait3D, which feature complex real-world covariates like varying backgrounds and clothing) and ‘in-the-lab’ controlled environments (CASIA-B and OU-MVLP). The results consistently demonstrate that GaitRDAE achieves state-of-the-art performance. For example, on the GREW dataset, GaitRDAE significantly outperformed previous methods in Rank-1 accuracy, showing its robustness in real-world conditions. Similarly, on Gait3D, it showed superior performance in Rank-1, Rank-5, and mAP metrics.
Ablation studies confirmed the individual and combined effectiveness of the RDA and RDE modules. The RDA module alone led to substantial improvements in accuracy, highlighting the importance of adaptive temporal patterns. The RDE module also contributed positively, validating the benefit of dynamic excitation. When combined, RDA and RDE showed even more significant gains, indicating their complementary roles in enhancing the model’s ability to capture complex motion patterns.
Visualizations further illustrate GaitRDAE’s capabilities. One visualization showed how the model adaptively assigns longer temporal receptive fields (represented by red regions) to motion-rich areas like hands and feet, and shorter fields (blue regions) to static areas. Another visualization using t-SNE demonstrated that GaitRDAE produces more compact intra-class feature distributions and clearer inter-class separation, meaning it can better distinguish between different individuals based on their gait.
While GaitRDAE marks a significant advancement, the use of 3D convolutions for searching receptive fields does increase the number of model parameters. Future work aims to explore model distillation and compression techniques, as well as 2D+1D decomposed modeling, to improve efficiency. Additionally, integrating the latest parsing-based methods could further enhance the understanding of motion regions. This research, detailed in the paper available at arxiv.org/pdf/2510.16541, represents a crucial step towards more robust and practical gait recognition systems in complex environments.
