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Naturalistic driving data (NDD) is an important source of information tounderstand crash causation and human factors and to further develop crashavoidance countermeasures. Videos recorded while driving are often included insuch datasets. While there is often a large amount of video data in NDD, only asmall 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 visionmethod to automatically extract the information we need from videos. Morespecifically, we developed a 3D ConvNet algorithm to automatically extractcell-phone-related behaviors from videos. The experiments show that our methodcan extract chunks from videos, most of which (~79%) contain the automaticallylabeled cell phone behaviors. In conjunction with human review of the extractedchunks, this approach can find cell-phone-related driver behaviors much moreefficiently than simply viewing video.

Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets