Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets

• URL: http://arxiv.org/abs/2505.15176v3
• Date: Mon, 26 May 2025 08:03:56 GMT
• Title: Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets
• Authors: Qian Zhou, Xianda Guo, Jilong Wang, Chuanfu Shen, Zhongyuan Wang, Hua Zou, Qin Zou, Chao Liang, Long Chen, Gang Wu,
• Abstract summary: Generalized gait recognition aims to achieve robust performance across diverse domains.<n>Mixed-dataset training is widely used to enhance generalization.<n>We propose a unified framework that systematically improves cross-domain gait recognition.
• Score: 24.242460774158463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized gait recognition, which aims to achieve robust performance across diverse domains, remains a challenging problem due to severe domain shifts in viewpoints, appearances, and environments. While mixed-dataset training is widely used to enhance generalization, it introduces new obstacles including inter-dataset optimization conflicts and redundant or noisy samples, both of which hinder effective representation learning. To address these challenges, we propose a unified framework that systematically improves cross-domain gait recognition. First, we design a disentangled triplet loss that isolates supervision signals across datasets, mitigating gradient conflicts during optimization. Second, we introduce a targeted dataset distillation strategy that filters out the least informative 20\% of training samples based on feature redundancy and prediction uncertainty, enhancing data efficiency. Extensive experiments on CASIA-B, OU-MVLP, Gait3D, and GREW demonstrate that our method significantly improves cross-dataset recognition for both GaitBase and DeepGaitV2 backbones, without sacrificing source-domain accuracy. Code will be released at https://github.com/li1er3/Generalized_Gait.

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