论文标题
带有3D CORVNET的自然主义驾驶数据集中的视频中的驾驶员行为提取
Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets
论文作者
论文摘要
自然主义驾驶数据（NDD）是了解崩溃因果关系和人为因素的重要信息来源，并进一步发展避免崩溃的对策。此类数据集经常包含驾驶时录制的视频。尽管NDD中通常有大量的视频数据，但其中只有一小部分可以由人类编码人员注释并用于研究，这在所有视频数据中都没有。在本文中，我们探索了一种计算机视觉方法，以自动从视频中提取所需的信息。更具体地说，我们开发了一种3D Convnet算法，以自动从视频中提取与手机相关的行为。实验表明，我们的方法可以从视频中提取块，其中大多数（约79％）包含自动标记的手机行为。结合对提取的块的人类评论，这种方法可以比仅观看视频更有效地发现与手机相关的驾驶员行为。
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.
