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HAUPT, D. HONZÍK, P. KUČERA, P. HYNČICA, O.
Original Title
CLASSIFICATION OF DRIVER'S DROWSINESS FROM STEERING WHEEL MOTION UNDER REAL TRAFFIC CONDITIONS
Type
conference paper
Language
English
Original Abstract
To develop a system for drivers drowsiness recognition is a challenging task in the modern car transportation. Many studies have promising results. Unfortunately most data is acquired in the laboratory conditions. Therefore proving drowsiness detection reliability and accuracy in real traffic is difficult. The analyzed data in this paper is acquired from the real traffic and hence it contains all uncertainty. An in-direct measurement from the vehicle CAN bus has been chosen for data acquisition in order to not affect the driver. The data is preprocessed according to the assumptions about drivers behavior and transformed to the frequency domain by means of the orthogonal transform (STFT, CWT and DWT). Subsequently, in the frequency domain, more than 70 000 features are generated. By means of the filter feature selection, 10 best features are chosen for a prediction. Finally, 1-NN model is used for prediction accuracy estimation.
Keywords
drowsiness, driver, Wavelet transform, Fourier transform, feature generation, AUC, LOOCV, CV
Authors
HAUPT, D.; HONZÍK, P.; KUČERA, P.; HYNČICA, O.
RIV year
2012
Released
27. 6. 2012
ISBN
978-80-214-4540-6
Book
MENDEL 2012, 18th International Conference on Soft Computing
Pages from
428
Pages to
433
Pages count
6
BibTex
@inproceedings{BUT93627, author="Daniel {Haupt} and Petr {Honzík} and Pavel {Kučera} and Ondřej {Hynčica}", title="CLASSIFICATION OF DRIVER'S DROWSINESS FROM STEERING WHEEL MOTION UNDER REAL TRAFFIC CONDITIONS", booktitle="MENDEL 2012, 18th International Conference on Soft Computing", year="2012", pages="428--433", isbn="978-80-214-4540-6" }