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Detecting driver inattention by rough iconic classification

Masala, Giovanni Luca Christian and Grosso, Enrico (2013) Detecting driver inattention by rough iconic classification. In: 2013 IEEE: Intelligent Vehicles Symposium (IV), 23-26 giugno 2013, Gold Coast City, Australia. [S.l.], IEEE. p. 913-918. ISSN 1931-0587. ISBN 978-1-4673-2754-1. Conference or Workshop Item.

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DOI: 10.1109/IVS.2013.6629583

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The paper proposes an original method, derived from basic face recognition and classification research, which is a good candidate for an effective automotive application. The proposed approach exploits a single b/w camera, positioned in front of the driver, and a very efficient classification strategy, based on neural network classifiers.
A peculiar feature of the work is the adoption of iconic data reduction, avoiding specific and time-consuming feature-based approaches. Though at an initial development stage, the method proved to be fast and robust compared to state of the art techniques; experimental results show real-time response and mean weighted accuracy near to 93%. The method requires a simple training procedure which can be certainly improved for real applications; moreover it can be easily integrated with techniques for automatic face-recognition of the driver.

Item Type:Conference or Workshop Item (Lecture)
ID Code:9945
Uncontrolled Keywords:Behavioural sciences computing, cameras, driver information systems, face recognition, image classification, neural nets
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
Deposited On:02 Jul 2014 13:27

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