Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3389/frobt.2017.00053 | FRONTIERS IN ROBOTICS AND AI |
Keywords | Field | DocType |
object detection,attention detection,visual attention mapping,head pose,3D point cloud,human-robot interaction | Computer vision,Object detection,Gaze,Computer science,Pose,Artificial intelligence,Probabilistic logic,Robot,Observer (quantum physics),Perception,Human–robot interaction | Journal |
Volume | ISSN | Citations |
4.0 | 2296-9144 | 1 |
PageRank | References | Authors |
0.36 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Veronese | 1 | 1 | 0.36 |
| Mattia Racca | 2 | 2 | 2.43 |
| Roel Pieters | 3 | 22 | 10.80 |
| V. Kyrki | 4 | 652 | 61.79 |