Visual Saliency Detection in Advanced Driver Assistance Systems
Francesco Rundo Michael Sebastian Rundo Concetto Spampinato
Visual Saliency refers to the innate human mechanism of focusing on and
extracting important features from the observed environment. Recently, there
has been a notable surge of interest in the field of automotive research
regarding the estimation of visual saliency. While operating a vehicle, drivers
naturally direct their attention towards specific objects, employing
brain-driven saliency mechanisms that prioritize certain elements over others.
In this investigation, we present an intelligent system that combines a
drowsiness detection system for drivers with a scene comprehension pipeline
based on saliency. To achieve this, we have implemented a specialized 3D deep
network for semantic segmentation, which has been pretrained and tailored for
processing the frames captured by an automotive-grade external camera. The
proposed pipeline was hosted on an embedded platform utilizing the STA1295
core, featuring ARM A7 dual-cores, and embeds an hardware accelerator.
Additionally, we employ an innovative biosensor embedded on the car steering
wheel to monitor the driver drowsiness, gathering the PhotoPlethysmoGraphy
(PPG) signal of the driver. A dedicated 1D temporal deep convolutional network
has been devised to classify the collected PPG time-series, enabling us to
assess the driver level of attentiveness. Ultimately, we compare the determined
attention level of the driver with the corresponding saliency-based scene
classification to evaluate the overall safety level. The efficacy of the
proposed pipeline has been validated through extensive experimental results.