Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While there is often a large amount of video data in NDD, only a small portion of them can be annotated by human coders and used for research, which underuses all video data. In this paper, we explored a computer vision method to automatically extract the information we need from videos. More specifically, we developed a 3D ConvNet algorithm to automatically extract cell-phone-related behaviors from videos. The experiments show that our method can extract chunks from videos, most of which (~79%) contain the automatically labeled cell phone behaviors. In conjunction with human review of the extracted chunks, this approach can find cell-phone-related driver behaviors much more efficiently than simply viewing video.
翻译:自然驱动数据(NDD)是了解坠机因果关系和人为因素以及进一步制定避免坠机对策的重要信息来源。在驾驶时记录的视频通常包含在这样的数据集中。虽然在NDD中通常有大量视频数据,但只有一小部分可以由人类代码员附加说明,用于研究,而研究则没有充分利用所有视频数据。在本文中,我们探索了计算机视觉方法,以自动从视频中提取我们所需要的信息。更具体地说,我们开发了3D ConvNet算法,从视频中自动提取与手机有关的行为。实验显示,我们的方法可以从视频中提取块块块,其中多数(~79%)包含自动标签的手机行为。在对提取的块进行人类审查的同时,这一方法可以比简单地查看视频更有效地找到与手机有关的驱动程序。