Benchmarking of Computer Vision Algorithms for Driver Monitoring on Automotive-grade Devices

The continuing evolution of technologies in the automotive industry has led to the development of the so-called Advanced Driver Assistance Systems (ADAS). ADAS is the term used to describe vehicle-based intelligent safety systems designed to support the driver, with the aim to significantly improve his safety, and the driving safety in general. In terms of development, current ADAS technologies are based on control functions about the vehicle movements with respect to the objects and entities detected in the same environment (e.g., other vehicles, pedestrian, roads, etc.). However, there is an ever growing interest on the use of internal cameras to infer additional information regarding the driver status (e.g., weakness, level of attention). The purpose of such technologies is to provide accurate details about the environment in order to increase safety and smart driving. In the last few years, Computer Vision technology has achieved impressive results on several tasks related to recognition and detection of customized objects/entities on images and videos. However, automotive-grade devices' hardware resources are limited, with respect to the once usually required for the implementation of modern Computer Vision algorithms. In this work, we present a benchmarking evaluation of a standard Computer Vision algorithm for the driver behaviour monitoring through face detection and analysis, comparing the performances obtained on a common laptop with the same experiments on an existing commercial automotive-grade device based on the Accordo5 processor by STMicroelectronics.