Abstract
Keeping a driver focused on the road is one of the
most critical steps in insuring the safe operation of a vehicle.
The Strategic Highway Research Program 2 (SHRP2) has over
3,100 recorded videos of volunteer drivers during a period of 2
years. This extensive naturalistic driving study (NDS) contains
over one million hours of video and associated data that could
aid safety researchers in understanding where the driver’s
attention is focused. Manual analysis of this data is infeasible,
therefore efforts are underway to develop automated feature
extraction algorithms to process and characterize the data.
The real-world nature, volume, and acquisition conditions are
unmatched in the transportation community, but there are also
challenges because the data has relatively low resolution, high
compression rates, and differing illumination conditions. A
smaller dataset, the head pose validation study, is available
which used the same recording equipment as SHRP2 but is
more easily accessible with less privacy constraints. In this
work we report initial head pose accuracy using commercial
and open source face pose estimation algorithms on the head
pose validation data set.
