Abstract
Gait recognition enables contact-free, long-range person identification that is robust to clothing variations and non-cooperative scenarios. While existing methods perform well in controlled indoor environments, they struggle with cross-vertical view scenarios, where surveillance angles vary significantly in elevation. Our experiments show up to 60\% accuracy degradation in low-to-high vertical view settings due to severe deformations and self-occlusions of key anatomical features. Current CNN and self-attention-based methods fail to effectively handle these challenges, due to their reliance on single-scale convolutions or simplistic attention mechanisms that lack effective multi-frequency feature integration. To tackle this challenge, we propose CVVNet (Cross-Vertical-View Network), a frequency aggregation architecture specifically designed for robust cross-vertical-view gait recognition. CVVNet employs a High-Low Frequency Extraction module (HLFE) that adopts parallel multi-scale convolution/max-pooling path and self-attention path as high- and low-frequency mixers for effective multi-frequency feature extraction from input silhouettes. We also introduce the Dynamic Gated Aggregation (DGA) mechanism to adaptively adjust the fusion ratio of high- and low-frequency features. The integration of our core Multi-Scale Attention Gated Aggregation (MSAGA) module, HLFE and DGA enables CVVNet to effectively handle distortions from view changes, significantly improving the recognition robustness across different vertical views. Experimental results show that our CVVNet achieves state-of-the-art performance, with $8.6\%$ improvement on DroneGait and $2\%$ on Gait3D compared with the best existing methods.
Abstract (translated)
步态识别技术能够实现非接触式的长距离人员识别，对服装变化和非配合场景具有较强的鲁棒性。尽管现有的方法在受控的室内环境中表现良好，但在垂直视角变化显著的情况下（例如监控角度高度不同），它们的表现就会大打折扣。实验表明，在低到高垂直视角设置中，由于关键解剖特征出现严重变形和自我遮挡，步态识别准确率最多可下降60%。 目前基于卷积神经网络(CNN)和自注意力机制的方法因依赖单一尺度的卷积操作或简单的注意机制而难以有效应对上述挑战，这些机制缺乏有效的多频段特征整合。为解决这一难题，我们提出了一种专用于稳健垂直视角步态识别的频率聚合架构CVVNet（Cross-Vertical-View Network）。CVVNet采用了高低频提取模块(HLFE)，该模块采用平行多尺度卷积/最大池化路径和自注意力路径作为高频和低频混合器，以有效从输入轮廓中提取多频段特征。我们还引入了动态门控聚合(DGA)机制来适应性地调整高、低频特征的融合比率。 通过整合核心的多尺度注意门控聚合(MSAGA)模块、HLFE 和 DGA，CVVNet 能够有效地处理视角变化带来的扭曲问题，并显著提高不同垂直视角下的识别鲁棒性。实验结果表明，我们的 CVVNet 达到了最先进的性能，在 DroneGait 数据集上比现有最佳方法提高了 8.6%，在 Gait3D 数据集上提高了2%。
URL
https://arxiv.org/abs/2505.01837
