Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
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 detectio...
| Authors | Jessica Wenninger, Gabriel Skantze |
|---|---|
| Primary category | cs.RO |
| Published | 2 June 2026 |
| Last updated at source | 23 June 2026 |
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