Abstract
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 this https URL and is actively maintained. We will continue updating this report as we add new features.
Abstract (translated)
我们提出了 FastPoseGait,一个基于 PyTorch 的开源工具集,用于基于姿态的步态识别。该工具集支持一组最新的基于姿态的步态识别算法和相关基准。与专注于单一算法的其他基于姿态的项目不同,FastPoseGait在一个统一框架下集成了多个最先进的算法,包括最新的进展和最佳实践,以 ease 于效率和效力的比较。此外,为了促进基于姿态的步态识别研究的未来发展,我们提供了许多预训练模型和详细的基准结果,提供了有价值的 insights 并作为进一步研究的参考。通过利用 FastPoseGait 提供的高模块化结构和多种方法,研究人员可以快速深入基于姿态的步态识别,促进该领域的发展。在本文中,我们描述了该工具集的各种特性,旨在使我们的工具集和基准进一步促进合作、促进可重复性和鼓励开发基于姿态的步态识别的创新算法。FastPoseGait 可以在这个 https URL 上可用,并正在积极维护。我们将随着添加新特性继续更新本报告。
URL
https://arxiv.org/abs/2309.00794
