@mastersthesis {MSC1081,
	title = {Scene Layout Segmentation of Traffic Environments Using a Conditional Random Field},
	author = {Fernando Martinelli},
	year = {2010},
	abstract = {At least 80\% of the traffic accidents in the world are caused by human mistakes. Whether drivers are too tired, drunk or speeding, most accidents have their root in the improper behavior of drivers. Many of these accidents could be avoided if cars were equipped with some kind of intelligent system able to detect inappropriate actions of the driver and autonomously intervene by controlling the car in emergency situations. Such an advanced driver assistance system needs to be able to understand the car environment and, from that information, predict the appropriate behavior of the driver at every instant. In this thesis project we investigate the problem of scene understanding solely based on images from an off-the-shelf camera mounted to the car. A system has been implemented that is capable of performing semantic segmentation and classification of road scene video sequences. The object classes which are to be segmented can be easily defined as input parameters. Some important classes for the prediction of the driver behavior include {\textquoteleft}road{\textquoteright}, {\textquoteleft}sidewalk{\textquoteright}, {\textquoteleft}car{\textquoteright} and {\textquoteleft}building{\textquoteright}, for example. Our system is trained in a supervised manner and takes into account information such as color, location, texture and also spatial context between classes. These cues are integrated within a Conditional Random Field model, which offers several practical advantages in the domain of image segmentation and classification. The recently proposed CamVid database, which contains challenging inner-city road video sequences with very precise ground truth segmentation data, has been used for evaluating the quality of our segmentation, including a comparison to state-of-the-art methods.},
	address = {Girona},
	institution = {University of Girona}
}