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Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems

arXiv 2307.13600 ↗First posted 25 Jul 2023cs.NE cs.CVcited by 0

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Abstract (arXiv)

Optical sensors have played a pivotal role in acquiring real world data for critical applications. This data, when integrated with advanced machine learning algorithms provides meaningful information thus enhancing human vision. This paper focuses on various optical technologies for design and development of state-of-the-art out-cabin forward vision systems and in-cabin driver monitoring systems. The focused optical sensors include Longwave Thermal Imaging (LWIR) cameras, Near Infrared (NIR), Neuromorphic/ event cameras, Visible CMOS cameras and Depth cameras. Further the paper discusses different potential applications which can be employed using the unique strengths of each these optical modalities in real time environment.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 25 Jul 2023.

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9 of 32 references.

References · 9

  1. 2020Real-Time Face & Eye Tracking and Blink Detection using Event Camerascited by 27
  2. 2023Shining light on the DVS pixel: A tutorial and discussion about biasing and optimizationcited by 21
  3. 2021Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)cited by 13
  4. 2022Control and Evaluation of Event Cameras Output Sharpness via Biascited by 10
  5. 2022Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systemscited by 9
  6. 2022Traffic Sign Detection With Event Cameras and DCNNcited by 5
  7. 2020Augmented Reality-Based Advanced Driver-Assistance System for Connected Vehiclescited by 4
  8. 2023Neuromorphic Sensing for Yawn Detection in Driver Drowsinesscited by 3
  9. 2022Event-based YOLO Object Detection: Proof of Concept for Forward Perception Systemcited by 2

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