论文标题
真实汽车解决方案的虚拟乘客：合成数据集
Virtual passengers for real car solutions: synthetic datasets
论文作者
论文摘要
包括生成合成数据的策略开始变得可行，因为获得真实数据可能在逻辑上很复杂，非常昂贵或缓慢。捕获数据不仅会导致并发症，而且会导致其注释。为了获得培训智能系统的高保真数据，我们建立了一个3D场景和设置，以尽可能地类似于现实。通过我们的方法，可以配置和改变参数以增加场景的随机性，并以这种方式允许数据变化，这在构建数据集中非常重要。此外，注释任务已经包含在数据生成练习中，而不是作为捕捉后任务，可以节省大量资源。我们介绍了在汽车上下文中的合成数据生成的过程和概念，特别是用于驾驶员和乘客监视目的，作为真实数据捕获的替代方案。
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.
