Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras

arXiv:2403.00196v1 Announce Type: cross
Abstract: Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities. However, these modalities operate at varying rates, posing challenges for real-time, comprehensive driver state monitoring. This paper addresses the issue of missing data due to sensor frame rate mismatches, introducing a generative model approach to create synthetic yet realistic thermal imagery. We propose using conditional generative adversarial networks (cGANs), specifically comparing …

abstract adas advanced advanced driver assistance application arxiv augmentation cameras challenges combination cs.ai cs.cv cs.lg data driver framework general images intelligent perception sensing synthetic synthetic data systems type vehicles video