Gait recognition is an emerging biological recognition technology that
identifies and verifies individuals based on their walking patterns. However,
many current methods are limited in their use of temporal information. In order
to fully harness the potential of gait recognition, it is crucial to consider
temporal features at various granularities and spans. Hence, in this paper, we
propose a novel framework named GaitGS, which aggregates temporal features in
the granularity dimension and span dimension simultaneously. Specifically,
Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing
the micro-motion and macro-motion information at the frame level and unit level
respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to
generate global and local temporal representations. On three popular gait
datasets, extensive experiments demonstrate the state-of-the-art performance of
our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0%
(+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The
source code will be released soon.

本论文提出了一种名为 GaitGS 的新型框架，利用多颗粒度和跨度、多个时间特征提取器和其他模块来进行步态识别，并在三个流行的数据集上进行了广泛的实验，证明了我们方法的最新性能。