Mercury: A Vision-Based Framework for Driver Monitoring

Abstract

In this paper, we propose a complete framework, namely Mercury, that combines Computer Vision and Deep Learning algorithms to continuously monitor the driver during the driving activity. The proposed solution complies to the requirements imposed by the challenging automotive context: the light invariance, in order to have a system able to work regardless of the time of day and the weather conditions. Therefore, infrared-based images, i.e. depth maps (in which each pixel corresponds to the distance between the sensor and that point in the scene), have been exploited in conjunction with traditional intensity images. Second, the non-invasivity of the system is required, since driver’s movements must not be impeded during the driving activity: in this context, the use of cameras and vision-based algorithms is one of the best solutions. Finally, real-time performance is needed since a monitoring system must immediately react as soon as a situation of potential danger is detected.

Recommended citation

@inproceedings{borghi2020mercury,
  title={Mercury: a vision-based framework for Driver Monitoring},
  author={Borghi, Guido and Pini, Stefano and Vezzani, Roberto and Cucchiara, Rita},
  booktitle={International Conference on Intelligent Human Systems Integration},
  pages={104--110},
  year={2020},
  organization={Springer}
}