Probabilistic Mapping of Human Visual Attention from Head Pose Estimation

Andrea Veronese, Mattia Racca, Roel Pieters, Ville Kyrki

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
244 Downloads (Pure)

Abstract

Effective interaction between a human and a robot requires the bidirectional perception and interpretation of actions and behavior. While actions can be identified as a directly observable activity, this might not be sufficient to deduce actions in a scene. For example, orienting our face toward a book might suggest the action toward “reading.” For a human observer, this deduction requires the direction of gaze, the object identified as a book and the intersection between gaze and book. With this in mind, we aim to estimate and map human visual attention as directed to a scene, and assess how this relates to the detection of objects and their related actions. In particular, we consider human head pose as measurement to infer the attention of a human engaged in a task and study which prior knowledge should be included in such a detection system. In a user study, we show the successful detection of attention to objects in a typical office task scenario (i.e., reading, working with a computer, studying an object). Our system requires a single external RGB camera for head pose measurements and a pre-recorded 3D point cloud of the environment.
Original languageEnglish
Article number53
Number of pages11
JournalFrontiers in Robotics and AI
Volume4
Issue numberOCT
DOIs
Publication statusPublished - 30 Oct 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Object detection
  • attention detection
  • visual attention mapping
  • head pose
  • human-robot interface

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