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Real time detection of driver attention: emerging solutions based on robust iconic classifiers and dictionary of poses

Masala, Giovanni Luca Christian and Grosso, Enrico (2014) Real time detection of driver attention: emerging solutions based on robust iconic classifiers and dictionary of poses. Transportation Research Part C: Emerging Technologies, Vol. 49 , p. 32-42. ISSN 0968-090X. eISSN 1879-2359. Article.

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DOI: 10.1016/j.trc.2014.10.005

Abstract

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.
In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.
On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.

Item Type:Article
ID Code:10651
Status:Published
Refereed:Yes
Uncontrolled Keywords:Automotive applications, monitoring of driver attention, driver assistance systems, neural networks
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Scienze Politiche, Scienze della Comunicazione e Ingegneria dell'Informazione
Publisher:Elsevier
ISSN:0968-090X
eISSN:1879-2359
Copyright Holders:© 2014 The Authors. Published by Elsevier Ltd.
Deposited On:20 Jan 2015 11:15

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