Neuro Computing Systems - EDDD: Event-Based Drowsiness Driving

Journal article

Guang Chen, Lin Hong, Jinhu Dong, Peigen Liu, J. Conradt, Alois Knoll
IEEE Sensors Journal, 2020

Cite

APA Click to copy
Chen, G., Hong, L., Dong, J., Liu, P., Conradt, J., & Knoll, A. (2020). EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor. IEEE Sensors Journal.

Chicago/Turabian Click to copy
Chen, Guang, Lin Hong, Jinhu Dong, Peigen Liu, J. Conradt, and Alois Knoll. “EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor.” IEEE Sensors Journal (2020).

MLA Click to copy
Chen, Guang, et al. “EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor.” IEEE Sensors Journal, 2020.

BibTeX Click to copy

@article{guang2020a,
  title = {EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor},
  year = {2020},
  journal = {IEEE Sensors Journal},
  author = {Chen, Guang and Hong, Lin and Dong, Jinhu and Liu, Peigen and Conradt, J. and Knoll, Alois}
}

Abstract

Drowsiness driving is a principal factor of many fatal traffic accidents. This paper presents the first event-based drowsiness driving detection (EDDD) system by using the recently developed neuromorphic vision sensor. Compared with traditional frame-based cameras, neuromorphic vision sensors, such as Dynamic Vision Sensors (DVS), have a high dynamic range and do not acquire full images at a fixed frame rate but rather have independent pixels that output intensity changes (called events) asynchronously at the time they occur. Since events are generated by moving edges in the scene, DVS is considered as an efficient and effective detector for the drowsiness driving-related motions. Based on this unique output, this work first proposes a highly efficient method to recognize and localize the driver’s eyes and mouth motions from event streams. We further design and extract event-based drowsiness-related features directly from the event streams caused by eyes and mouths motions, then the EDDD model is established based on these features. Additionally, we provide the EDDD dataset, the first public dataset dedicated to event-based drowsiness driving detection. The EDDD dataset has 260 recordings in daytime and evening with several challenging scenes such as subjects wearing glasses/sunglasses. Experiments are conducted based on this dataset and demonstrate the high efficiency and accuracy of our method under different illumination conditions. As the first investigation of the usage of DVS in drowsiness driving detection applications, we hope that this work will inspire more event-based drowsiness driving detection research.