The analysis of patterns of walking is an important area of research that has numerous applications in security, healthcare, sports and human-computer interaction. Lately, walking patterns have been regarded as a unique fingerprinting method for automatic person identification at a distance. In this work, we propose a novel gait recognition architecture called Gait Pyramid Transformer (GaitPT) that leverages pose estimation skeletons to capture unique walking patterns, without relying on appearance information. GaitPT adopts a hierarchical transformer architecture that effectively extracts both spatial and temporal features of movement in an anatomically consistent manner, guided by the structure of the human skeleton. Our results show that GaitPT achieves state-of-the-art performance compared to other skeleton-based gait recognition works, in both controlled and in-the-wild scenarios. GaitPT obtains 82.6% average accuracy on CASIA-B, surpassing other works by a margin of 6%. Moreover, it obtains 52.16% Rank-1 accuracy on GREW, outperforming both skeleton-based and appearance-based approaches.

翻译：步态模式分析是安全、医疗、体育及人机交互等领域的重要研究方向。近年来，步态模式被视为一种独特的远距离自动身份识别指纹方法。本文提出一种新颖的步态识别架构——步态金字塔变换器（GaitPT），该架构利用姿态估计骨架捕捉独特的步态模式，无需依赖外观信息。GaitPT 采用层级式变换器架构，在人体骨骼结构引导下，以解剖学一致的方式有效提取运动的空间与时间特征。研究结果表明，在受控与野外场景下，GaitPT 在基于骨架的步态识别方法中均达到最优性能。GaitPT 在 CASIA-B 数据集上取得 82.6% 的平均准确率，超过其他方法 6%；在 GREW 数据集上取得 52.16% 的 Rank-1 准确率，优于基于骨架和基于外观的方法。