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Detecting driver inattention by rough iconic classification

Masala, Giovanni Luca Christian and Grosso, Enrico (30 July 2012) Detecting driver inattention by rough iconic classification. Alghero, University of Sassari - Computer Vision Laboratory. p. 3-12 (CVL 2012/002). Technical Report.

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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 peculiarity 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 92%. 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:Technical Report
ID Code:7864
Uncontrolled Keywords:Automotive safety, computer vision, visual attention, pattern classication, 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
001 Università di Sassari > 02 Centri > Computer Vision Laboratory
Publisher:University of Sassari - Computer Vision Laboratory
Deposited On:31 Jul 2012 09:30

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