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

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.

• The paper introduces FastPoseGait, a unified toolbox facilitating fair comparison of state-of-the-art pose-based gait recognition methods.
• It incorporates diverse algorithms and datasets, offering pre-trained models and benchmark results, including up to 95.02% rank-1 accuracy on CASIA-B.
• The toolbox’s modular design and efficient training strategies enable extensible research in both academic and real-world gait recognition applications.

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

The paper introduces FastPoseGait, an open-source toolbox designed to facilitate research and development in pose-based gait recognition. This toolbox consolidates various state-of-the-art (SOTA) algorithms under a unified framework, which enhances the ability to compare their effectiveness and efficiency. One of its primary aims is to bridge existing gaps in the comparison of different pose-based methods across both indoor and outdoor datasets, as well as diverse scales of data.

The authors emphasize the modularity and extensibility of FastPoseGait, allowing researchers to easily adapt and extend the framework for their needs. This modularity spans several essential areas including model architecture, sampling strategies, data processing techniques, and various loss function implementations. It provides a comprehensive platform, making it a valuable resource for the advancement of pose-based gait recognition.

Key Contributions

1. Unified Framework for Comparison: FastPoseGait provides a standardized platform for implementing and testing pose-based algorithms. By aligning experimental settings, including batch sizes and network architectures, it enables fair comparisons among different methods, which is crucial for establishing a clear benchmark in the field.
2. Diverse Algorithm and Benchmark Support: FastPoseGait supports a variety of cutting-edge pose-based gait recognition algorithms such as GaitGraph, GaitGraph2, and GaitTR. It is evaluated across multiple datasets, including CASIA-B, OUMVLP-Pose, GREW, and Gait3D, thus covering a broad spectrum of scenarios from indoor environments to real-world settings.
3. Pre-trained Models and Benchmark Results: The toolbox offers numerous pre-trained models along with comprehensive benchmark results, providing solid baselines for further research. This aspect significantly reduces the overhead for researchers new to the field, allowing them to focus on developing novel methods without the need to start from scratch.
4. High Efficiency and Versatility: FastPoseGait implements various advanced training strategies such as Distributed Data Parallelism and Auto Mixed Precision, which enhance its efficiency. This makes it suitable for large-scale training involved in extensive datasets like OUMVLP-Pose and GREW.

Numerical Results and Evaluations

The paper provides a detailed evaluation of the supported algorithms. Noteworthy among the results is the demonstration of the adaptability of pose-based methods across different datasets and conditions. For instance, GaitTR achieves a notable rank-1 accuracy of up to 95.02\% under the Normal condition on the CASIA-B dataset. The toolbox's capability for running unified experiments is particularly beneficial in elucidating performance differences and enhancing reproducibility in scientific evaluation.

Implications and Future Directions

FastPoseGait stands as an important tool in the advancement of pose-based gait recognition, offering a robust infrastructure that encourages collaboration and reproducibility in the field. The availability of pre-trained models and detailed benchmark results provides a valuable resource for the community, likely accelerating development and innovation. The authors note that FastPoseGait is continuously evolving, with plans for future updates to incorporate new features and algorithms.

Practically, the toolbox could extend its usage beyond academic research to real-world applications where robust gait recognition under varying environments is necessary, such as security and surveillance. Theoretically, it poses several questions regarding the boundary conditions under which pose-based recognition systems achieve optimal performance.

Conclusion

In summary, this paper introduces a comprehensive toolbox, FastPoseGait, designed to streamline and strengthen research in pose-based gait recognition. By providing a unified and highly modular framework, the toolbox not only facilitates the comparison of existing methods but also nurtures further exploration and development of innovative pose-based algorithms. The contributions of FastPoseGait lie in its capacity to enhance reproducibility and collaborative effort within the research community, indicating a promising trajectory for future research in the area of gait recognition.