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 for Gait REcognition in the Wild (GREW). The GREW
dataset is constructed from natural videos, which contains hundreds of cameras
and thousands of hours 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 natural 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. Representative appearance-based and
model-based methods are explored, and comprehensive baselines are established.
Experimental results show (1) The proposed GREW benchmark is necessary for
training and evaluating gait recognizer in the wild. (2) For state-of-the-art
gait recognition approaches, there is a lot of room for improvement. (3) The
GREW benchmark can be used as effective pre-training for controlled gait
recognition. Benchmark website is this https URL

本文提供了新的野外步态识别基准 GREW，该基准数据集采用自然视频捕捉，并包含数百个摄像机和成千上万小时的流。GREW 包含 26k 个身份和 128k 个序列，具有丰富的属性用于非受控步态识别。该基准可作为 state-of-the-art 步态识别算法的有效预训练，旨在促进野外步态识别技术的发展。