Abstract
In recent years, traffic accidents caused by the distracted driving have been on the rise with the popularization of smart phones. How to correctly identify whether the driver is in a distracted driving state and to provide the necessary warnings for the driver to avoid potential safety risks has become one of the most concerned issues. In this paper, a distracted driving recognition method based on deep convolutional neural network is proposed for the driving image data captured by the in-vehicle camera. This method uses the PCA technology to whiten the driving image, which reduces the redundancy and correlation of the pixel matrix. At the same time, a multi-layer CNN network is constructed in the model and the key parameters of the input layer, convolution layer, pooling layer, fully connected layer and output layer are optimized as well. The results of experimental analysis show that the accuracy of the proposed method can reach 97.31%, which is higher than that of the existing machine learning algorithms. Therefore, the proposed method is effective in improving the accuracy of distracted driving recognition.
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Funding
This work is supported in part by the National Nature Science Foundation of China (Grant No. 61841701), the GuangDong Natural Science Foundation (Grant No. 2019B010137002) and the Research Project of Fuzhou Polytechnic (Grant No. FZYKJJJB201901).
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Rao, X., Lin, F., Chen, Z. et al. Distracted driving recognition method based on deep convolutional neural network. J Ambient Intell Human Comput 12, 193–200 (2021). https://doi.org/10.1007/s12652-019-01597-4
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DOI: https://doi.org/10.1007/s12652-019-01597-4