Abstract

Distracted driver action is the main cause of road traffic crashes, which threatens the security of human life and public property. Based on the observation that cues (like the hand holding the cigarette) reveal what the driver is doing, a driver action recognition model is proposed, which is called deformable and dilated Faster R-CNN (DD-RCNN). Our approach utilizes the detection of motion-specific objects to classify driver actions exhibiting great intra-class differences and inter-class similarity. Firstly, deformable and dilated residual block are designed to extract features of action-specific RoIs that are small in size and irregular in shape (such as cigarettes and cell phones). Attention modules are embedded in the modified ResNet to reweight features in channel and spatial dimensions. Then, the region proposal optimization network (RPON) is presented to reduce the number of RoIs entering R-CNN and improves model efficiency. Lastly, the RoI pooling module is replaced with the deformable one, and the simplified R-CNN without regression layer is trained as the final classifier. Experiments show that DD-RCNN demonstrates state-of-the-art results on Kaggle-driving dataset and self-built dataset.

中文翻译：

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使用变形和扩张更快的R-CNN与优化的区域建议进行驾驶员动作识别

摘要

分心的驾驶员行为是造成道路交通事故的主要原因，它威胁着人类生命和公共财产的安全。基于观察到的提示（如握着香烟的手）揭示了驾驶员在做什么，提出了驾驶员动作识别模型，称为可变形且扩张的Faster R-CNN（DD-RCNN）。我们的方法利用运动特定对象的检测来对表现出巨大的类内差异和类间相似性的驾驶员动作进行分类。首先，设计可变形和扩张的残留块来提取特定于动作的RoI的特征，这些RoI的尺寸小且形状不规则（例如香烟和手机）。注意模块嵌入在经过修改的ResNet中，以对通道和空间尺寸中的特征重新加权。然后，提出了区域提议优化网络（RPON），以减少进入R-CNN的RoI数量并提高模型效率。最后，将RoI合并模块替换为可变形模块，并将没有回归层的简化R-CNN训练为最终分类器。实验表明，DD-RCNN在Kaggle驾驶数据集和自建数据集上展示了最先进的结果。