用单目深度相机实时识别多人互动，让机器人更懂社交地导航
Real-time Recognition of Human Interactions from a Single RGB-D Camera for Socially-Aware Robot Navigation
- 通过RGB-D相机提取3D人体关键点，结合PCA判断互动方向
- 用鞋带公式计算互动焦点区域，准确识别不同人数的群体互动
- 每帧仅需4毫秒，可直接接入机器人系统，适合真实场景部署
人类互动识别对社交机器人在共享环境中的安全自然导航至关重要。传统机器人系统多关注避障，忽视了实现流畅人机交互所必需的社会线索。为此，本文提出一种面向社交感知导航的人群互动识别框架。该方法利用单目RGB-D相机的颜色与深度图像，估计三维人体关键点和位置；通过主成分分析（PCA）确定主要互动方向；最后采用鞋带公式计算兴趣点与参与区域。大量实验验证了该方法在不同场景、不同人数下的有效性。结果表明，该方法具备高实时性，在机器人常用单板计算机上每帧处理时间约为4毫秒。系统已实现为ROS 2包，便于集成至现有导航系统。源码公开于https://github.com/thanhlong103/social-interaction-detector。
原文摘要 · Abstract (English)
{Recognizing human interactions is essential for social robots as it enables them to navigate safely and naturally in shared environments. Conventional robotic systems however often focus on obstacle avoidance, neglecting social cues necessary for seamless human-robot interaction. To address this gap, we propose a framework to recognize human group interactions for socially aware navigation. Our method utilizes color and depth frames from a monocular RGB-D camera to estimate 3D human keypoints and positions. Principal component analysis (PCA) is then used to determine dominant interaction directions. The shoelace formula is finally applied to compute interest points and engagement areas. Extensive experiments have been conducted to evaluate the validity of the proposed method. The results show that our method is capable of recognizing group interactions across different scenarios with varying numbers of individuals. It also achieves high-speed performance, processing each frame in approximately 4 ms on a single-board computer used in robotic systems. The method is implemented as a ROS 2 package making it simple to integrate into existing navigation systems. Source code is available at https://github.com/thanhlong103/social-interaction-detector
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