  Gait recognition is a biometric technique that identifies individuals by
their unique walking styles, which is suitable for unconstrained environments
and has a wide range of applications. While current methods focus on exploiting
body part-based representations, they often neglect the hierarchical
dependencies between local motion patterns. In this paper, we propose a
hierarchical spatio-temporal representation learning (HSTL) framework for
extracting gait features from coarse to fine. Our framework starts with a
hierarchical clustering analysis to recover multi-level body structures from
the whole body to local details. Next, an adaptive region-based motion
extractor (ARME) is designed to learn region-independent motion features. The
proposed HSTL then stacks multiple ARMEs in a top-down manner, with each ARME
corresponding to a specific partition level of the hierarchy. An adaptive
spatio-temporal pooling (ASTP) module is used to capture gait features at
different levels of detail to perform hierarchical feature mapping. Finally, a
frame-level temporal aggregation (FTA) module is employed to reduce redundant
information in gait sequences through multi-scale temporal downsampling.
Extensive experiments on CASIA-B, OUMVLP, GREW, and Gait3D datasets demonstrate
that our method outperforms the state-of-the-art while maintaining a reasonable
balance between model accuracy and complexity.
