Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognition, academia is restricted by current existing databases captured in the controlled environment. In this paper, we contribute a new benchmark and strong baseline for Gait REcognition in the Wild (GREW). The GREW dataset is constructed from natural videos, which contain hundreds of cameras and thousands of hours of streams in open systems. With tremendous manual annotations, the GREW consists of 26K identities and 128K sequences with rich attributes for unconstrained gait recognition. Moreover, we add a distractor set of over 233K sequences, making it more suitable for real-world applications. Compared with prevailing predefined cross-view datasets, the GREW has diverse and practical view variations, as well as more naturally challenging factors. To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild. Equipped with this benchmark, we dissect the unconstrained gait recognition problem, where representative appearance-based and model-based methods are explored. The proposed GREW benchmark proves to be essential for both training and evaluating gait recognizers in unconstrained scenarios. In addition, we propose the Single Path One-Shot neural architecture search with uniform sampling for Gait recognition, named SPOSGait, which is the first NAS-based gait recognition model. In experiments, SPOSGait achieves state-of-the-art performance on the CASIA-B, OU-MVLP, Gait3D, and GREW benchmarks, outperforming existing approaches by a large margin. The code will be released at https://github.com/XiandaGuo/SPOSGait.

翻译：步态基准测试为研究社区训练和评估高性能步态识别系统提供了基础。尽管学界在跨视角识别方面投入了越来越多的努力，但现有数据库均采集于受控环境，限制了学术研究的发展。本文提出了一种用于现实场景步态识别（GREW）的新型基准测试与强基线模型。GREW数据集基于自然视频构建，这些视频来自开放系统中的数百个摄像头及数千小时的监控流。经过大量人工标注，该数据集包含2.6万个身份和12.8万个序列，具备丰富的属性特征，适用于无约束步态识别。此外，我们额外引入了超过23.3万个干扰序列，使其更贴合实际应用场景。与当前主流的预定义跨视角数据集相比，GREW呈现出多样化的实际视角变化，并包含更多自然挑战因素。据我们所知，这是首个面向现实场景的大规模步态识别数据集。借助该基准，我们剖析了无约束步态识别问题，并探索了具有代表性的基于外观和基于模型的方法。实验证明，所提出的GREW基准对无约束场景下的步态识别器训练与评估均至关重要。此外，我们提出了基于均匀采样的单路径一次性神经架构搜索方法用于步态识别，命名为SPOSGait，这是首个基于NAS的步态识别模型。在实验中，SPOSGait在CASIA-B、OU-MVLP、Gait3D和GREW基准测试中均取得了最先进的性能，大幅超越现有方法。代码将开源于https://github.com/XiandaGuo/SPOSGait。