Visual saliency refers to the part of the visual scene in which the subject’s gaze is focused, allowing significant applications in various fields including automotive. Indeed, the car driver decides to focus on specific objects rather than others by deterministic brain-driven saliency mechanisms inherent perceptual activity. In the automotive industry, vision saliency estimation is one of the most common technologies in Advanced Driver Assistant Systems (ADAS). In this work, we proposed an intelligent system consisting of: (1) an ad-hoc Non-Local Semantic Segmentation Deep Network to process the frames captured by automotive-grade camera device placed outside the car, (2) an innovative bio-sensor to perform car driver PhotoPlethysmoGraphy (PPG) signal sampling for monitoring related drowsiness and, (3) ad-hoc designed 1D Temporal Deep Convolutional Network designed to classify the so collected PPG time-series providing an assessment of the driver attention level. A downstream check-block verifies if the car driver attention level is adequate for the saliency-based scene classification. Our approach is extensively evaluated on DH1FK dataset, and experimental results show the effectiveness of the proposed pipeline.
Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving
Rundo F.;Trenta F.;Bellitto G.;Salanitri F. P.;Genovese A.;Spampinato C.;Battiato S.
2021-01-01
Abstract
Visual saliency refers to the part of the visual scene in which the subject’s gaze is focused, allowing significant applications in various fields including automotive. Indeed, the car driver decides to focus on specific objects rather than others by deterministic brain-driven saliency mechanisms inherent perceptual activity. In the automotive industry, vision saliency estimation is one of the most common technologies in Advanced Driver Assistant Systems (ADAS). In this work, we proposed an intelligent system consisting of: (1) an ad-hoc Non-Local Semantic Segmentation Deep Network to process the frames captured by automotive-grade camera device placed outside the car, (2) an innovative bio-sensor to perform car driver PhotoPlethysmoGraphy (PPG) signal sampling for monitoring related drowsiness and, (3) ad-hoc designed 1D Temporal Deep Convolutional Network designed to classify the so collected PPG time-series providing an assessment of the driver attention level. A downstream check-block verifies if the car driver attention level is adequate for the saliency-based scene classification. Our approach is extensively evaluated on DH1FK dataset, and experimental results show the effectiveness of the proposed pipeline.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
