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06 October 2024 |
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Article overview
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Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection | | Paul Yudkin
; Eli Friedman
; Orly Zvitia
; Gil Elbaz
; | | Date: |
1 Jun 2022 | | 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. | | Source: | arXiv, 2206.00148 | | Services: | Forum | Review | PDF | Favorites | |
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