面向人机交互的面部与身体跟踪：一个自我中心数据集
Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
AI总结：
针对社交机器人自我中心视角下频繁身份切换问题，提出一个自定义标注的自我中心数据集，通过系统评估检测误差、对比面部与身体跟踪，并分析扩展空间记忆和外观重识别的影响，最终优化管道将身份切换减少49%。
摘要：
为了实现有意义的人机交互（HRI），机器人必须通过持续跟踪用户来不断评估参与度。然而，最先进的计算机视觉模型主要针对监控或自动驾驶进行了优化。社交机器人面临独特的自我中心挑战，例如人类跳动、相互遮挡或离开画面。频繁的身份切换（IDSW）会导致机器人在对话中失去立足点。为了解决这个问题，我们引入了一个新颖的、自定义标注的自我中心数据集，通过Furhat机器人收集，以捕捉复杂的社会动态。我们进行了系统评估，将检测错误与跟踪逻辑分离，比较面部与身体跟踪，并评估扩展空间记忆和外观重识别（ReID）的影响。结果表明，增加空间记忆可以缓解长时间遮挡，但在复杂动态事件上失败。集成ReID解决了复杂的切换，但表现出相反的效果：它显著提高了身体跟踪的稳定性，但由于轮廓角度敏感性导致面部IDSW激增。最终，我们的优化管道将IDSW减少了49%，减轻了交互中断。由于标准基准缺乏密集的近距离遮挡，这项工作强调了原生捕捉社会动态对于真正验证HRI感知模型的迫切需求。
英文摘要：
Meaningful human-robot interaction (HRI) requires a robot to continuously assess user engagement through persistent user tracking. However, state-of-the-art Multi-Object Tracking models are heavily optimized for surveillance or autonomous driving. A social robot faces distinct egocentric challenges, such as humans moving in unpredictable nonlinear patterns, obstructing each other, or leaving and reentering the scene. These dynamics trigger frequent identity switches (IDSW), causing the robot to lose its footing mid-conversation. To address this, we introduce a focused, custom-annotated egocentric dataset collected via the Furhat robot. We present a systematic evaluation isolating detection errors from tracking logic, comparing face versus body tracking, and assessing the impact of extended memory and appearance re-identification (ReID). Results indicate that increasing temporal memory mitigates prolonged occlusions but fails on complex dynamic events. Integrating ReID resolves complex switches but exhibits opposing effects: it substantially improves body tracking stability, yet causes facial IDSW to spike due to profile angle sensitivity. Ultimately, our optimized pipeline reduces IDSW by 49% compared to a standard tracking-by-detection baseline, effectively mitigating interaction breakdowns. As standard benchmarks lack dense, close-quarter occlusions, this work highlights the critical need for natively captured social dynamics to truly validate HRI perception models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
