Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and physics-based ray-tracing rendering in order to represent the world in 3D and create highly realistic images. Datagen has specialized in the generation of high-quality 3D humans, realistic 3D environments and generation of realistic human motion. This technology has been developed into a data generation platform which we used for these experiments. This work demonstrates the use of synthetic photo-realistic in-cabin data to train a Driver Monitoring System that uses a lightweight neural network to detect whether the driver's hands are on the wheel. We demonstrate that when only a small amount of real data is available, synthetic data can be a simple way to boost performance. Moreover, we adopt the data-centric approach and show how performing error analysis and generating the missing edge-cases in our platform boosts performance. This showcases the ability of human-centric synthetic data to generalize well to the real world, and help train algorithms in computer vision settings where data from the target domain is scarce or hard to collect.
翻译:过去几年来,在利用模拟技术的合成数据生成领域取得了重大进展。这些方法使用高端图形引擎和基于物理的射线采集工具,在3D中代表世界,并制作高度现实的图像。数据元专门制造高质量的3D人、现实的3D环境和产生现实的人类运动。这一技术已经发展成一个数据生成平台,我们用于这些实验。这项工作表明利用合成的摄影现实化成成成像的对子宫内数据来培训一个司机监测系统,该系统使用轻量神经网络来检测驾驶员是否在轮子上。我们证明,当只有少量真实数据可用时,合成数据可以成为提高性能的简单方法。此外,我们采用了以数据为中心的方法,并展示如何进行错误分析,并在我们的平台推进性能中生成缺失的边缘情况。这展示了以人为中心的合成数据能够对真实世界进行普及,并帮助在目标域数据稀缺或难以收集的数据的计算机视觉设置中培训算法。