TY  - CONF
AU  - German Ros
AU  - Sebastian Ramos
AU  - Manuel Granados
AU  - Amir Bakhtiary
AU  - David Vazquez
AU  - Antonio Lopez
A2  - WACV
PY  - 2015//
TI  - Vision-based Offline-Online Perception Paradigm for Autonomous Driving
BT  - IEEE Winter Conference on Applications of Computer Vision
SP  - 231
EP  - 238
KW  - Autonomous Driving
KW  - Scene Understanding
KW  - SLAM
KW  - Semantic Segmentation
N2  - Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
UR  - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7045624
L1  - http://refbase.cvc.uab.es/files/rrg2015.pdf
UR  - http://dx.doi.org/10.1109/WACV.2015.38
N1  - ADAS; 600.076
ID  - German Ros2015
ER  -