Abstract
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.
Abstract (translated)
步态识别是一项新兴的生物识别技术，基于个人步态特征来识别和验证个体。然而，许多当前的方法在使用时间信息方面的限制很大。为了充分利用步态识别的潜力，必须考虑不同粒度和跨度的时间特征。因此，在本文中，我们提出了一种名为 GaitGS 的新框架，该框架同时聚合粒度和跨度方面的时间特征。具体来说，我们建议采用 Multi-Granularity 特征提取器(MGFE)，专注于捕捉帧级和单元级上的微动和 macro-Motion 信息。此外，我们还提出了 Multi-Span 特征学习模块，以生成全球和 local 时间表示。在三个流行的步态数据集上，广泛实验展示了我们方法的先进性能。我们的方法在 CASIA-B、GREW 和 OU-MVLP 等数据集上分别实现了 92.9%、52.0% 和 97.5%的排名第一精度。源代码将很快发布。
URL
https://arxiv.org/abs/2305.19700
