The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ from the training data. In this paper, we introduce a robust model designed to withstand changes in camera position within the vehicle. Our Driver Behavior Monitoring Network (DBMNet) relies on a lightweight backbone and integrates a disentanglement module to discard camera view information from features, coupled with contrastive learning to enhance the encoding of various driver actions. Experiments conducted on the daytime and nighttime subsets of the 100-Driver dataset validate the effectiveness of our approach with an increment on average of 9\% in Top-1 accuracy in comparison with the state of the art. In addition, cross-dataset and cross-camera experiments conducted on three benchmark datasets, namely AUCDD-V1, EZZ2021 and SFD, demonstrate the superior generalization capability of the proposed method.

翻译：分心驾驶行为分类对于保障行车安全至关重要。先前研究已证明神经网络在自动预测驾驶员分心、疲劳及潜在危险方面的有效性。然而，近期研究发现，当这些模型应用于与训练数据采集条件不同的样本时，会出现显著的精度下降。本文提出一种鲁棒模型，旨在适应车内摄像头位置的变化。我们的驾驶员行为监测网络（DBMNet）采用轻量级骨干架构，集成特征解耦模块以消除特征中的摄像头视角信息，并结合对比学习增强各类驾驶动作的编码表征。在100-Driver数据集的日间与夜间子集上进行的实验验证了本方法的有效性，其Top-1准确率较现有最优方法平均提升9%。此外，通过在AUCDD-V1、EZZ2021和SFD三个基准数据集上进行的跨数据集与跨摄像头实验，证明了所提方法具有优异的泛化能力。