Virtual passengers for real car solutions: synthetic datasets
- URL: http://arxiv.org/abs/2205.06556v1
- Date: Fri, 13 May 2022 10:54:39 GMT
- Title: Virtual passengers for real car solutions: synthetic datasets
- Authors: Paola Natalia Canas, Juan Diego Ortega, Marcos Nieto and Oihana
Otaegui
- Abstract summary: We build a 3D scenario and set-up to resemble reality as closely as possible.
It is possible to configure and vary parameters to add randomness to the scene.
We present the process and concept of synthetic data generation in an automotive context.
- Score: 2.1028463367241033
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Strategies that include the generation of synthetic data are beginning to be
viable as obtaining real data can be logistically complicated, very expensive
or slow. Not only the capture of the data can lead to complications, but also
its annotation. To achieve high-fidelity data for training intelligent systems,
we have built a 3D scenario and set-up to resemble reality as closely as
possible. With our approach, it is possible to configure and vary parameters to
add randomness to the scene and, in this way, allow variation in data, which is
so important in the construction of a dataset. Besides, the annotation task is
already included in the data generation exercise, rather than being a
post-capture task, which can save a lot of resources. We present the process
and concept of synthetic data generation in an automotive context, specifically
for driver and passenger monitoring purposes, as an alternative to real data
capturing.
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