GaitPT: Skeletons Are All You Need For Gait Recognition

TL;DR

GaitPT introduces a hierarchical transformer architecture that uses skeleton pose data for gait recognition, achieving state-of-the-art accuracy without appearance cues in various scenarios.

Contribution

The paper presents GaitPT, a novel skeleton-based gait recognition model utilizing a hierarchical transformer to improve accuracy over existing methods.

Findings

Achieves 82.6% accuracy on CASIA-B, surpassing previous skeleton-based methods.

Obtains 52.16% Rank-1 accuracy on GREW, outperforming both skeleton and appearance-based approaches.

Effective in both controlled and in-the-wild scenarios.

Abstract

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.