@article{ref1,
title="Virtual and real world adaptation for pedestrian detection",
journal="IEEE transactions on pattern analysis and machine intelligence",
year="2013",
author="Vazquez, David and López, Antonio M. and Marin, Javier and Ponsa, Daniel and Gerónimo, David",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing performance in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection performance than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.<p /> <p>Language: en</p>",
language="en",
issn="0162-8828",
doi="01C55106-D14F-4365-A376-31978AFCCBAC",
url="http://dx.doi.org/01C55106-D14F-4365-A376-31978AFCCBAC"
}