  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.
