基于卷积神经网络的驾驶员不安全行为识别

提出了一种基于卷积神经网络的驾驶员违规行为识别方法。首先，利用特定卷积神经网络对驾驶员的实时图像提取特征，然后并行对多种行为分别进行二分类。建立了一个真实场景下的驾驶员违规数据集，在此数据集上的测试说明了该方法的高效和良好的泛化能力。实验结果表明，该方法在约10万张图像的数据集中对打电话、吸烟、不系安全带3种行为分别达到了99.85%、99.62%、98.68%的识别率，同时使用当前较先进的Inception-v3和Xception模型测试，也获得了类似的识别效果。

Recognition of Unsafe Driving Behaviors Based on Convolutional Neural Network

The unsafe behavior of the driver is one of the important causes of many incidents. This paper presents a method to recognize unsafe driving behaviors based on the convolutional neural network. Firstly, the characteristics of the real-time image are extracted by the specific convolutional neural network, and then three kinds of behaviors are classified into two categories in parallel. The data set of unsafe driving behaviors in a real scene is established. The test on this dataset illustrates the efficiency and good generalization of the method. The experimental results show that the method achieves 99.85%, 99.62% and 98.68% accuracy for calling, smoking and unbelting in the data set of about 100 000 images, which is comparable to the results obtained by recent advanced Inception-v3 and Xception models.