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

TL;DR

This paper demonstrates that synthetic, photo-realistic in-cabin data generated via simulation can effectively train driver monitoring systems, especially when real data is limited, by leveraging a data-centric approach to improve model performance.

Contribution

The study introduces a method to use high-quality synthetic data for training driver hand detection models, showing improvements with limited real data and emphasizing error analysis and edge-case generation.

Findings

Synthetic data boosts model performance with scarce real data.

Error analysis and edge-case generation improve detection accuracy.

Synthetic data generalizes well to real-world scenarios.

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.