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Authors: Francesco Rundo 1 ; Roberto Leotta 2 ; Francesca Trenta 2 ; Giovanni Bellitto 3 ; Federica Salanitri 3 ; Vincenzo Piuri 4 ; Angelo Genovese 4 ; Ruggero Labati 4 ; Fabio Scotti 4 ; Concetto Spampinato 3 and Sebastiano Battiato 2

Affiliations: 1 STMicroelectronics, ADG Central R&D, Italy ; 2 University of Catania, IPLAB Group, Italy ; 3 University of Catania, PerCeiVe Lab, Italy ; 4 University of Milan, Computer Science Department, Italy

Keyword(s): Drowsiness, Deep Learning, D-CNN, Deep-LSTM, PPG (PhotoPlethysmoGraphy).

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. (More)

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Paper citation in several formats:
Rundo, F.; Leotta, R.; Trenta, F.; Bellitto, G.; Salanitri, F.; Piuri, V.; Genovese, A.; Labati, R.; Scotti, F.; Spampinato, C. and Battiato, S. (2021). Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving. In Proceedings of the International Conference on Image Processing and Vision Engineering - IMPROVE, ISBN 978-989-758-511-1, pages 81-90. DOI: 10.5220/0010381000810090

@conference{improve21,
author={Francesco Rundo. and Roberto Leotta. and Francesca Trenta. and Giovanni Bellitto. and Federica Salanitri. and Vincenzo Piuri. and Angelo Genovese. and Ruggero Labati. and Fabio Scotti. and Concetto Spampinato. and Sebastiano Battiato.},
title={Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving},
booktitle={Proceedings of the International Conference on Image Processing and Vision Engineering - IMPROVE,},
year={2021},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010381000810090},
isbn={978-989-758-511-1},
}

TY - CONF

JO - Proceedings of the International Conference on Image Processing and Vision Engineering - IMPROVE,
TI - Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving
SN - 978-989-758-511-1
AU - Rundo, F.
AU - Leotta, R.
AU - Trenta, F.
AU - Bellitto, G.
AU - Salanitri, F.
AU - Piuri, V.
AU - Genovese, A.
AU - Labati, R.
AU - Scotti, F.
AU - Spampinato, C.
AU - Battiato, S.
PY - 2021
SP - 81
EP - 90
DO - 10.5220/0010381000810090

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