Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems. However, most existing methods make use of only the video from a single view and the difficulty-inconsistent issue is neglected. Different from them, in this work, we propose a novel MultI-camera Feature Integration (MIFI) approach for 3D distracted driver activity recognition by jointly modeling the data from different camera views and explicitly re-weighting examples based on their degree of difficulty. Our contributions are two-fold: (1) We propose a simple but effective multi-camera feature integration framework and provide three types of feature fusion techniques. (2) To address the difficulty-inconsistent problem in distracted driver activity recognition, a periodic learning method, named example re-weighting that can jointly learn the easy and hard samples, is presented. The experimental results on the 3MDAD dataset demonstrate that the proposed MIFI can consistently boost performance compared to single-view models.

翻译：摘要：分心驾驶行为识别在风险规避中具有关键作用——特别是在智能交通系统中应用广泛。然而，现有方法大多仅利用单一视角视频，且忽视了难度不一致性问题。与此不同，本工作提出了一种新颖的多相机特征集成方法（MIFI），通过联合建模不同相机视角的数据并基于样本难度显式调整权重，实现三维分心驾驶行为识别。我们的贡献包含两点：（1）提出简单有效的多相机特征集成框架，并提供三种特征融合技术；（2）针对分心驾驶行为识别中的难度不一致问题，提出一种名为样本重加权的周期性学习方法，可联合学习简单与困难样本。在3MDAD数据集上的实验结果表明，与单视角模型相比，所提出的MIFI方法能够持续提升性能。