FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait
  Recognition
Shibei Meng Yang Fu Saihui Hou Chunshui Cao Xu Liu Yongzhen Huang
We present FastPoseGait, an open-source toolbox for pose-based gait
recognition based on PyTorch. Our toolbox supports a set of cutting-edge
pose-based gait recognition algorithms and a variety of related benchmarks.
Unlike other pose-based projects that focus on a single algorithm, FastPoseGait
integrates several state-of-the-art (SOTA) algorithms under a unified
framework, incorporating both the latest advancements and best practices to
ease the comparison of effectiveness and efficiency. In addition, to promote
future research on pose-based gait recognition, we provide numerous pre-trained
models and detailed benchmark results, which offer valuable insights and serve
as a reference for further investigations. By leveraging the highly modular
structure and diverse methods offered by FastPoseGait, researchers can quickly
delve into pose-based gait recognition and promote development in the field. In
this paper, we outline various features of this toolbox, aiming that our
toolbox and benchmarks can further foster collaboration, facilitate
reproducibility, and encourage the development of innovative algorithms for
pose-based gait recognition. FastPoseGait is available at
https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will
continue updating this report as we add new features.