Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Existing methods show impressive results on individual datasets but 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. UDA for Gait Recognition is still in its infancy and existing works proposed solutions to limited scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of gait recognition models, in which the target domain is biased to pose-based features rather than identity features, causing a significant performance drop in the identification task. We suggest Gait Orientation-based method for Unsupervised Domain Adaptation (GOUDA) to reduce this bias. To this end, we present a novel Triplet Selection algorithm with a curriculum learning framework, aiming to adapt the embedding space by pushing away samples of similar poses and bringing closer samples of different poses. 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, showing the superiority of our proposed method over prior works.

翻译：步态识别是一项计算机视觉任务，旨在通过行走模式识别个体身份。现有方法在单个数据集上表现显著，但缺乏泛化至未见场景的能力。无监督域适应（UDA）旨在将源域上以监督方式预训练的模型适配至无标签的目标域。步态识别的无监督域适应尚处于初期阶段，现有工作仅针对有限场景提出解决方案。本文揭示了步态识别模型适配中的一个基本现象：目标域倾向于关注基于姿态的特征而非身份特征，导致身份识别任务性能显著下降。我们提出基于步态朝向的无监督域适应方法（GOUDA）以减少这一偏差。为此，我们提出一种新颖的三元组选择算法，结合课程学习框架，通过推开相似姿态样本、拉近不同姿态样本来调整嵌入空间。我们在四个广泛使用的步态数据集（CASIA-B、OU-MVLP、GREW、Gait3D）及三种骨干网络（GaitSet、GaitPart、GaitGL）上进行了大量实验，结果表明我们的方法优于先前工作。