Name
Abstract | ||
|---|---|---|
In this work, we were interested in creating a robot service for offering help to people who appear to be in need for guidance. In order to achieve that, we first developed a technique to detect pedestrians who walk in an atypical way (e.g., people who do not know their way). In our approach, a motion model of typical pedestrians developed in our previous work was used, and a novel predictability feature was defined that quantifies how well can a person's future position be predicted using that model. The classification method based on this feature gave accurate results and outperformed alternative methods. Using this detection method, we created a robot service for offering guidance to people who were classified as atypical. Experiments done in a shopping mall have shown that the robot was successful in choosing the people to approach, and the reactions from users in the interviews were very positive. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TRO.2016.2645206 | IEEE Trans. Robotics |
Keywords | Field | DocType |
Trajectory,Predictive models,Computational modeling,Data collection,Robot sensing systems,Cameras | Computer vision,Data collection,Predictability,Personal robot,Control engineering,Human–computer interaction,Artificial intelligence,Engineering,Robot,Trajectory,Shopping mall | Journal |
Volume | Issue | ISSN |
33 | 2 | 1552-3098 |
Citations | PageRank | References |
1 | 0.37 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Drazen Brscic | 1 | 148 | 10.38 |
| Tetsushi Ikeda | 2 | 95 | 9.94 |
| Takayuki Kanda | 3 | 3477 | 326.97 |