Name
Abstract | ||
|---|---|---|
The ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICARCV.2010.5707333 | Control Automation Robotics & Vision |
Keywords | Field | DocType |
Bayes methods,object detection,sensors,target tracking,traffic engineering computing,ArosDyn project,Bayesian fusion,car driving,collision risks prediction,dynamic scenes,embedded software,heterogenous sensor data,lidars,multiple moving object detection,multiple moving object tracking,onboard telemetric sensors,robust analysis,stereo camera,urban traffic environments,visual sensors,Bayesian filter,Mobile robot,collision risk,lidar,sensor fusion,stereo vision,traffic environment | Stereo camera,Object detection,Computer vision,Embedded software,Computer science,Stereopsis,Collision,Sensor fusion,Artificial intelligence,Probabilistic logic,Mobile robot | Conference |
ISSN | ISBN | Citations |
2474-2953 | 978-1-4244-7814-9 | 3 |
PageRank | References | Authors |
0.60 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor E. Paromtchik | 1 | 118 | 12.63 |
| Christian Laugier | 2 | 27 | 3.49 |
| Mathias Perrollaz | 3 | 191 | 12.90 |
| Yong, M.Y. | 4 | 3 | 0.60 |
| Amaury Nègre | 5 | 124 | 8.88 |
| Christopher Tay | 6 | 97 | 5.86 |