Abstract
In this paper, an end-to-end deep learning solution for driver distraction recognition is presented. In the proposed framework, the features from pre-trained convolutional neural networks VGG-19 are extracted. Despite the variation in illumination conditions, camera position, driver’s ethnicity, and genders in our dataset, our best fine-tuned model, VGG-19 has achieved the highest test accuracy of 95% and an average accuracy of 80% per class. The model is tested with leave-one-driver-out cross validation method to ensure generalization. The results show that our framework avoided the overfitting problem which typically occurs in low-variance datasets. A comparison between our framework with the state-of-the-art XGboost shows that the proposed approach outperforms XGBoost in accuracy by approximately 7%.
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Acknowledgment
The authors would like to thank Dr. Alaa Khamis from Suez University, Egypt for his generous assistance with the data collection process.
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Koesdwiady, A., Bedawi, S.M., Ou, C., Karray, F. (2017). End-to-End Deep Learning for Driver Distraction Recognition. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_2
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DOI: https://doi.org/10.1007/978-3-319-59876-5_2
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