Name
Abstract | ||
|---|---|---|
Despite their popularity, occupancy grids cannot be directly applied to problems where the identity of the objects populating an environment needs to be taken into account (eg object tracking, scene interpretation, etc), in this cases it is necessary to postprocess the grid in order to extract object information. This paper approaches the problem by proposing a novel algo- rithm inspired on image segmentation techniques. The proposed approach works without prior knowledge about the number of objects to be detected and, at the same time, is very fast. This is possible thanks to the use of a novel Self Organizing Network (SON) coupled with a dynamic threshold. Our experimental results on both real and simulated data show that our approach is robust and able to operate at normal camera framerate. Keywords—Vision, Tracking, Image segmentation, Bayesian Occupancy Grid |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICARCV.2006.345364 | Singapore |
Keywords | Field | DocType |
Bayes methods,computer vision,feature extraction,image segmentation,object detection,self-organising feature maps,target tracking,Bayesian occupancy grid,computer vision,dynamic threshold,image segmentation,object detection,object extraction,object information,object tracking,scene interpretation,self organizing networks,Bayesian Occupancy Grid,Image segmentation,Tracking,Vision | Object detection,Computer vision,Pattern recognition,Computer science,Self-organizing network,Feature extraction,Image segmentation,Video tracking,Frame rate,Artificial intelligence,Grid,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2474-2953 | 1-4214-042-1 | 3 |
PageRank | References | Authors |
0.44 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dizan Vasquez | 1 | 172 | 12.76 |
| Fabrizio Romanelli | 2 | 3 | 0.44 |
| Thierry Fraichard | 3 | 866 | 70.04 |
| Christian Laugier | 4 | 199 | 12.49 |