Abstract

Deep Learning requires huge amount of data with related labels, that are necessary for proper training. Thanks to modern videogames, which aim at photorealism, it is possible to easily obtain synthetic dataset by extracting information directly from the game engine. The intent is to use data extracted from a videogame to obtain a representation of various scenarios and train a deep neural network to infer the information required for a specific task. In this work we focus on computer vision aids for automotive applications and we target to estimate the distance and speed of the surrounding vehicles by using a single dashboard camera. We propose two network models for distance and speed estimation, respectively. We show that training them by using synthetic images generated by a game engine is a viable solution that turns out to be very effective in real settings.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-030-30642-7_35 under the license http://www.springer.com/tdm
https://dblp.uni-trier.de/db/conf/iciap/iciap2019-1.html#ZaffaroniGF19,
https://link.springer.com/chapter/10.1007%2F978-3-030-30642-7_35,
https://rd.springer.com/chapter/10.1007/978-3-030-30642-7_35,
https://academic.microsoft.com/#/detail/2971590130
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1007/978-3-030-30642-7_35
Licence: CC BY-NC-SA license

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