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AbstractAccording to National Highway Traffic Safety Administration’s (NHTSA) official reports, many traffic accidents have been caused due to drivers’ drowsiness. Previous work based on computer vision techniques achieved drowsiness detection, usually with special hardware that depended on laboratory environments. To overcome limitations of these approaches, a natural light based surveillance system is proposed. The system achieves drowsiness detection in three stages: face segmentation, drowsiness feature extraction and classification. To segment faces, a simplified skin colour model is developed to compute colour distance maps from original facial images. Candidate faces are located using colour distance maps in conjunction with centres of gravity of individual faces. Gegenbauer features are then applied to capture shape information that is related to drowsiness. The computation of these features is based on moments derived from coefficients of Gegenbauer polynomials. To detect the behaviour of a subject, image sequences of his/her face are classified into drowsy and nondrowsy states by a Hidden Markov Model using Gegenbauer features. A sequence is classified as drowsy if the number of drowsy states in the Hidden Markov Model reaches a pre-defined threshold. To evaluate the proposed system, experiments are conducted using 65 video clips that contained a mixture of 54 drowsy and 11 non-drowsy behaviours. The proposed system detected 47 drowsy behaviours from these video clips successfully, and thus resulting in a detection rate of 87%. This proposed system is independent of infrared illuminators that were found to be unreliable in previous systems. Furthermore, the new system deploys multiple facial features and presents a more accurate description of drowsiness rather than a single facial feature proposed by previous authors.
PublisherUniversity of Bedfordshire
TypeThesis or dissertation
DescriptionA thesis submitted to the University of Bedfordshire, in partial fulfillment of the requirements for the degree of Doctor of Philosophy
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