@Inbook{GermanRos2017,
author="German Ros
and Laura Sellart
and Gabriel Villalonga
and Elias Maidanik
and Francisco Molero
and Marc Garcia
and Adriana Cede{\~n}o
and Francisco Perez
and Didier Ramirez
and Eduardo Escobar
and Jose Luis Gomez
and David Vazquez
and Antonio Lopez",
editor="Gabriela Csurka",
chapter="Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA",
title="Domain Adaptation in Computer Vision Applications",
year="2017",
publisher="Springer",
volume="12",
pages="227--241",
optkeywords="SYNTHIA",
optkeywords="Virtual worlds",
optkeywords="Autonomous Driving",
abstract="Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation -- in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation.",
optnote="ADAS; 600.085; 600.082; 600.076; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2882), last updated on Thu, 04 Apr 2019 12:38:08 +0200",
opturl="https://doi.org/10.1007/978-3-319-58347-1_12"
}