Abstract
Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the this http URL code is available at this https URL
Abstract (translated)
步态识别是一种新兴的身份验证技术，通过分析个体的行走模式在远距离下区分个人。传统的技术方法依赖于大规模标注数据集，这带来了高昂的成本和显著的标注挑战。近期，研究人员探索了基于聚类的无监督领域适应方法的无监督步态识别，并取得了显著的成功。然而，这些方法直接使用由聚类生成的伪标签，忽视了由于领域差异导致的伪标签噪声问题，从而影响模型训练过程的效果。 为了缓解这些问题，我们提出了一种名为GaitDCCR的新模型，旨在减少噪声音伪标签对聚类和模型训练的影响。我们的方法可以分为两个主要阶段：聚类阶段和训练阶段。在聚类阶段中，我们提出了动态簇参数（DCP）和动态权重中心（DWC），以提高聚类效率并获得可靠的簇质心。在训练阶段中，我们采用了经典的教师-学生结构，并提出基于置信度的伪标签精炼（CPR）以及对比式教师模块（CTM），鼓励噪声音样本收敛至包含其真实身份的簇内。 我们在公开的步态数据集上进行了广泛的实验，结果表明我们的简单而有效的方法显著提升了无监督步态识别的表现，为其实用化奠定了基础。相关代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2501.16608
