Name
Abstract | ||
|---|---|---|
The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in natural environments is a very important and challenging task. In this paper, a vision system to detect objects considering natural environments, able to work on real mobile robot is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Two approaches for implementing the selected classification method are explored, the prediction process one against all and one against one. The experimental results have demonstrated the usefulness of the system for detection and location of objects, and through the comparison of the two proposed approaches for the classification, has been determined which alternative offers better performance considering that the environment has not been changed, guaranteeing the naturalness of the place. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICARSC.2016.55 | 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC) |
Keywords | Field | DocType |
Object classification,object detection,Support Vector Machine,mobile robots,robots navigation | Computer vision,Machine vision,Naturalness,Support vector machine,Feature extraction,Image segmentation,Artificial intelligence,RGB color model,Mobile robot navigation,Engineering,Machine learning,Mobile robot | Conference |
ISSN | ISBN | Citations |
2573-9360 | 978-1-5090-2256-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alejandra Carolina Hernández | 1 | 6 | 5.54 |
| Clara Gómez | 2 | 6 | 4.86 |
| Jonathan Crespo | 3 | 19 | 4.10 |
| Ramon Barber | 4 | 10 | 3.78 |