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

arXiv: https://arxiv.org/abs/2206.00148 · HTML: https://mlsanity.com/paper/2206.00148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild](https://mlsanity.com/paper/2009.10013/) (2020)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands](https://mlsanity.com/paper/1804.07834/) (2018)
- [Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach](https://mlsanity.com/paper/1907.08009/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Survey of Data Synthesis Approaches](https://mlsanity.com/paper/2407.03672/) (2024)
- [Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing](https://mlsanity.com/paper/2303.15219/) (2023)
- [Face Recognition Using Synthetic Face Data](https://mlsanity.com/paper/2305.10079/) (2023)

## Abstract (arXiv)

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.