FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition

TL;DR

FastPoseGait is an open-source, modular toolbox that consolidates state-of-the-art pose-based gait recognition algorithms and benchmarks, facilitating research, comparison, and development in the field.

Contribution

It provides a unified framework with multiple algorithms, pre-trained models, and detailed benchmarks to advance pose-based gait recognition research.

Findings

Supports various SOTA algorithms within a unified framework

Provides extensive pre-trained models and benchmark results

Facilitates reproducibility and comparison in gait recognition research

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 https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will continue updating this report as we add new features.