  Social robots often rely on visual perception to understand their users and
the environment. Recent advancements in data-driven approaches for computer
vision have demonstrated great potentials for applying deep-learning models to
enhance a social robot's visual perception. However, the high computational
demands of deep-learning methods, as opposed to the more resource-efficient
shallow-learning models, bring up important questions regarding their effects
on real-world interaction and user experience. It is unclear how will the
objective interaction performance and subjective user experience be influenced
when a social robot adopts a deep-learning based visual perception model. We
employed state-of-the-art human perception and tracking models to improve the
visual perception function of the Pepper robot and conducted a controlled lab
study and an in-the-wild human-robot interaction study to evaluate this novel
perception function for following a specific user with other people present in
the scene.
