GPGait: Generalized Pose-based Gait Recognition
- URL: http://arxiv.org/abs/2303.05234v2
- Date: Tue, 15 Aug 2023 07:32:29 GMT
- Title: GPGait: Generalized Pose-based Gait Recognition
- Authors: Yang Fu, Shibei Meng, Saihui Hou, Xuecai Hu and Yongzhen Huang
- Abstract summary: Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods.
To improve the generalization ability of pose-based methods across datasets, we propose a textbfGeneralized textbfPose-based textbfGait recognition framework.
- Score: 11.316545213493223
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recent works on pose-based gait recognition have demonstrated the potential
of using such simple information to achieve results comparable to
silhouette-based methods. However, the generalization ability of pose-based
methods on different datasets is undesirably inferior to that of
silhouette-based ones, which has received little attention but hinders the
application of these methods in real-world scenarios. To improve the
generalization ability of pose-based methods across datasets, we propose a
\textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition
(\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a
series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified
pose representation with discriminative multi-features. Then, given the slight
variations in the unified representation after HOT and HOD, it becomes crucial
for the network to extract local-global relationships between the keypoints. To
this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to
enable efficient graph partition and local-global spatial feature extraction.
Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose,
Gait3D and GREW, show that our model demonstrates better and more stable
cross-domain capabilities compared to existing skeleton-based methods,
achieving comparable recognition results to silhouette-based ones. Code is
available at https://github.com/BNU-IVC/FastPoseGait.
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