Ensuring traffic safety and preventing accidents is a critical goal in daily
driving, where the advancement of computer vision technologies can be leveraged
to achieve this goal. In this paper, we present a multi-view, multi-scale
framework for naturalistic driving action recognition and localization in
untrimmed videos, namely M$^2$DAR, with a particular focus on detecting
distracted driving behaviors. Our system features a weight-sharing, multi-scale
Transformer-based action recognition network that learns robust hierarchical
representations. Furthermore, we propose a new election algorithm consisting of
aggregation, filtering, merging, and selection processes to refine the
preliminary results from the action recognition module across multiple views.
Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset
demonstrate the effectiveness of our approach, where we achieved an overlap
score of 0.5921 on the A2 test set. Our source code is available at
https://github.com/PurdueDigitalTwin/M2DAR.

提出了一个名为 M2DAR 的多视角、多尺度框架，利用计算机视觉技术实现自然驾驶动作识别和定位来保障交通安全，特别关注于检测分心驾驶行为，其核心特征在于一个具有权重共享、多尺度 Transformer-based 动作识别网络，并提出了一个新的选举算法从多个视角综合整合和选择动作识别模块的初步结果，实验表明该方法在第 7 届 AI City Challenge Track 3 数据集上实现了 0.5921 的重叠分数。