Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation, IEEE Transactions on Intelligent Transportation Systems, vol.7, issue.1, pp.20-37, 2006. ,
Fast visual road recognition and horizon detection using multiple artificial neural networks, IEEE Intelligent Vehicles Symposium (IV), pp.1090-1095, 2012. ,
DOI : 10.1109/ivs.2012.6232175
Road detection based on illuminant invariance, IEEE Transactions on Intelligent Transportation Systems, vol.12, issue.1, pp.184-193, 2011. ,
DOI : 10.1109/tits.2010.2076349
Crf-based semantic labeling in miniaturized road scenes, IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1902-1903, 2014. ,
Spatial ray features for real-time ego-lane extraction, IEEE Conference on Intelligent Transportation Systems (ITSC), pp.288-293, 2012. ,
Deep Deconvolutional Networks for Scene Parsing, 2014. ,
Stacked hierarchical labeling, European Conference on Computer Vision (ECCV), pp.57-70, 2010. ,
DOI : 10.1007/978-3-642-15567-3_5
URL : http://www.cs.cmu.edu/%7Edmunoz/pubs/munoz_eccv_10.pdf
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, vol.36, issue.4, pp.193-202, 1980. ,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf
Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp.1097-1105, 2012. ,
DOI : 10.1145/3065386
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf
Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, pp.1929-1958, 2014. ,
(a) Patch size 10x10. (b) Patch size 18x18. (c) Patch size 34x34, 2013. ,
Classification results using different patches sizes where green represents true positive, red false negative and blue false positive, vol.4 ,
A new performance measure and evaluation benchmark for road detection algorithms, IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1693-1700, 2013. ,
Caffe: Convolutional architecture for fast feature embedding, 2014. ,
Stixelnet: A deep convolutional network for obstacle detection and road segmentation, 26TH British Machine Vision Conference (BMVC), 2015. ,
Vision-based road detection using contextual blocks, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01260443
Crf based road detection with multi-sensor fusion, IEEE Intelligent Vehicles Symposium (IV), pp.192-198, 2015. ,
DOI : 10.1109/ivs.2015.7225685
A probabilistic distribution approach for the classification of urban roads in complex environments, Workshop on IEEE International Conference on Robotics and Automation (ICRA), 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01089086