Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Although existing methods often show high performance on specific datasets, they lack the ability to generalize to unseen scenarios. Unsupervised Domain Adaptation (UDA) tries to adapt a model, pre-trained in a supervised manner on a source domain, to an unlabelled target domain. There are only a few works on UDA for gait recognition proposing solutions to limited scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of gait recognition models, caused by the bias in the target domain to viewing angle or walking direction. We then suggest a remedy to reduce this bias with a novel triplet selection strategy combined with curriculum learning. To this end, we present Gait Orientation-based method for Unsupervised Domain Adaptation (GOUDA). We provide extensive experiments on four widely-used gait datasets, CASIA-B, OU-MVLP, GREW, and Gait3D, and on three backbones, GaitSet, GaitPart, and GaitGL, justifying the view bias and showing the superiority of our proposed method over prior UDA works.

翻译：步态识别是一项旨在通过行走模式识别个体的计算机视觉任务。尽管现有方法在特定数据集上常表现出高性能，但其泛化至未见场景的能力不足。无监督域适应（UDA）尝试将以监督方式在源域预训练的模型适应至无标签的目标域。目前仅有少数针对步态识别的UDA研究提出了针对有限场景的解决方案。本文揭示了步态识别模型适应过程中的一个根本现象，该现象由目标域对视角或行走方向的偏差所导致。我们进而提出一种结合课程学习的新型三元组选择策略以缓解此偏差。为此，我们提出了基于步态方向的无监督域适应方法（GOUDA）。我们在四个广泛使用的步态数据集（CASIA-B、OU-MVLP、GREW、Gait3D）及三个主干网络（GaitSet、GaitPart、GaitGL）上进行了大量实验，验证了视角偏差的存在，并证明了我们提出的方法优于先前的UDA工作。