Zaffaroni et al 2019a - Revision history

Scipediacontent at 15:12, 21 January 2021

2021-01-21T15:12:28Z

Scipediacontent: Scipediacontent moved page Draft Content 523232709 to Zaffaroni et al 2019a

2020-09-29T07:44:18Z

Scipediacontent moved page Draft Content 523232709 to Zaffaroni et al 2019a

Scipediacontent: Created page with " == 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..."

2020-09-29T07:44:15Z

Created page with " == 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..."

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== 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.

Document type: Part of book or chapter of book

== 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 http://dx.doi.org/10.1007/978-3-030-30642-7_35]

* [http://hdl.handle.net/11583/2750857 http://hdl.handle.net/11583/2750857]

* [http://hdl.handle.net/2318/1719738 http://hdl.handle.net/2318/1719738]

* [https://iris.unito.it/bitstream/2318/1719738/2/Camera%20Ready%20paper.pdf https://iris.unito.it/bitstream/2318/1719738/2/Camera%20Ready%20paper.pdf]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35 http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35],[http://dx.doi.org/10.1007/978-3-030-30642-7_35 http://dx.doi.org/10.1007/978-3-030-30642-7_35] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007%2F978-3-030-30642-7_35 https://link.springer.com/chapter/10.1007%2F978-3-030-30642-7_35],[https://academic.microsoft.com/#/detail/2971590130 https://academic.microsoft.com/#/detail/2971590130]

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 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.
@@ Line 19: / Line 17: @@
 * [https://iris.unito.it/bitstream/2318/1719738/2/Camera%20Ready%20paper.pdf https://iris.unito.it/bitstream/2318/1719738/2/Camera%20Ready%20paper.pdf]
-* [http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35 http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35],[http://dx.doi.org/10.1007/978-3-030-30642-7_35 http://dx.doi.org/10.1007/978-3-030-30642-7_35] under the license http://www.springer.com/tdm
+* [http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35 http://link.springer.com/content/pdf/10.1007/978-3-030-30642-7_35],
-* [https://link.springer.com/chapter/10.1007%2F978-3-030-30642-7_35 https://link.springer.com/chapter/10.1007%2F978-3-030-30642-7_35],[https://academic.microsoft.com/#/detail/2971590130 https://academic.microsoft.com/#/detail/2971590130]
+* [https://www.scipedia.com/public/Zaffaroni_et_al_2019a https://www.scipedia.com/public/Zaffaroni_et_al_2019a],

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