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<p>Nowadays, legged robots often use speaker recognition or Ultra Wide Band(UWB) positioning to identify and track certain people. However, these identification methods need the active cooperation of the identified person, which limits their application. To solve this problem, we propose a “Symmetry-encoding and pseudo-Centroid loss optimized Gait recognition method” (SCGait). The experimental results show that our gait recognition method achieves a mean test accuracy of 82.2% on three subsets of the CASIA-B dataset, and the ablation studies show that our gait encoding method and loss function have good generalization ability. Next, we combine SCGait with Yolo to develop a person identification-tracking system for legged robots. The experimental results show that our system performs quite well in both home companion and industrial patrol legged robots. It achieves an identification accuracy of 91.8% and a FPS of 36 in our test video in a multi-person scene. The code is available at <a href="https://github.com/qplqplqpl/SCGait">https://github.com/qplqplqpl/SCGait</a>.</p>

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SCGait a novel method for person identification applied to legged robots

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

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  • Penglin Qin,
  • Guanghua Xu,
  • Qingqiang Wu,
  • Fan Wei,
  • Yihua Zhao,
  • Kai Zhang

摘要

Nowadays, legged robots often use speaker recognition or Ultra Wide Band(UWB) positioning to identify and track certain people. However, these identification methods need the active cooperation of the identified person, which limits their application. To solve this problem, we propose a “Symmetry-encoding and pseudo-Centroid loss optimized Gait recognition method” (SCGait). The experimental results show that our gait recognition method achieves a mean test accuracy of 82.2% on three subsets of the CASIA-B dataset, and the ablation studies show that our gait encoding method and loss function have good generalization ability. Next, we combine SCGait with Yolo to develop a person identification-tracking system for legged robots. The experimental results show that our system performs quite well in both home companion and industrial patrol legged robots. It achieves an identification accuracy of 91.8% and a FPS of 36 in our test video in a multi-person scene. The code is available at https://github.com/qplqplqpl/SCGait.