CVVNet: A Cross-Vertical-View Network for Gait Recognition

• URL: http://arxiv.org/abs/2505.01837v2
• Date: Mon, 14 Jul 2025 09:00:31 GMT
• Title: CVVNet: A Cross-Vertical-View Network for Gait Recognition
• Authors: Xiangru Li, Wei Song, Yingda Huang, Wei Meng, Le Chang,
• Abstract summary: We propose CVVNet, a frequency aggregation architecture for robust cross-vertical-view gait recognition.<n>CVVNet achieves state-of-the-art performance, with $8.6%$ improvement on DroneGait and $2%$ on Gait3D.
• Score: 3.9124245851778032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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