Name
Abstract | ||
|---|---|---|
Deictic gestures - pointing at things in human-human collaborative tasks - constitute a pervasive, non-verbal way of communication, used e.g. to direct attention towards objects of interest. In a human-robot interactive scenario, in order to delegate tasks from a human to a robot, one of the key requirements is to recognize and estimate the pose of the pointing gesture. Standard approaches rely on full-body or partial-body postures to detect the pointing direction. We present a probabilistic, appearance-based object detection framework to detect pointing gestures and robustly estimate the pointing direction. Our method estimates the pointing direction without assuming any human kinematic model. We propose a functional model for pointing which incorporates two types of pointing, finger pointing and tool pointing using an object in hand. We evaluate our method on a new dataset with 9 participants pointing at 10 objects. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DICTA.2015.7371296 | 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
human-robot interaction,deictic gestures,human-human collaborative tasks,pointing gesture pose estimation,probabilistic appearance-based object detection framework,pointing gesture detection,finger pointing,tool pointing | Computer vision,Object detection,Kinematics,Computer science,Gesture,Delegate,Gesture recognition,Artificial intelligence,Probabilistic logic,Robot,Human–robot interaction | Conference |
Citations | PageRank | References |
7 | 0.47 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dadhichi Shukla | 1 | 21 | 3.11 |
| Özgür Erkent | 2 | 26 | 4.96 |
| Justus H. Piater | 3 | 543 | 61.56 |