论文标题
驱动网络：用于驾驶员分心检测的卷积网络
Drive-Net: Convolutional Network for Driver Distraction Detection
论文作者
论文摘要
为了防止汽车事故，人们对寻找一种自动化方法引起了极大的兴趣，以识别驾驶员分心的迹象，例如与乘客交谈，修理头发和化妆，饮食和饮食以及使用手机。在本文中，我们提出了一种称为驱动器网络的自动监督学习方法，用于驾驶员分心检测。驱动网络使用卷积神经网络（CNN）和随机决策林的组合来对驾驶员进行分类。我们将提议的驱动器网络的性能与其他两种流行的机器学习方法进行了比较：复发性神经网络（RNN）和多层感知器（MLP）。我们测试了在受控环境中获取的图像的公开数据库中，该方法包含由专家手动注释的大约22425张图像。结果表明，驱动网络的检测准确性为95％，比使用其他方法在同一数据库上获得的最佳结果高2％
To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approaches: a recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the methods on a publicly available database of images acquired under a controlled environment containing about 22425 images manually annotated by an expert. Results show that Drive-Net achieves a detection accuracy of 95%, which is 2% more than the best results obtained on the same database using other methods
