Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture
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Emine Cengil [1] ; Ahmet Cinar [1]
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[1] Fırat University
Fırat University
Turquía
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- Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 264-274
- Idioma: inglés
- Enlaces
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Resumen
Traffic safety is a problem that concerns the worldwide. Many traffic accidents occur. There are many situations that cause these accidents. However, when we look at the relevant statistics, it is seen that the traffic accident is caused by the behavior of the driver. Drivers who exhibit careless behavior, cause an accident. Preliminary detection of such actions may prevent the accident. In this study, it is possible to recognize the behavior of the state farm distracted driver detection data, which includes nine situations and one normal state image, which may cause an accident. The images are preprocessed with the LOG (Laplasian of Gaussian) filter. The feature extraction process is carried out with googlenet, which is the convolutional neural network architecture. As a result, the classification process resulted in 97.7% accuracy.
