Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection

• URL: http://arxiv.org/abs/2206.00148v1
• Date: Tue, 31 May 2022 23:34:12 GMT
• Title: Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection
• Authors: Paul Yudkin, Eli Friedman, Orly Zvitia, Gil Elbaz
• Abstract summary: This work demonstrates the use of synthetic photo-realistic in-cabin data to train a Driver Monitoring System. We 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.
• Score: 0.38233569758620045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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