  A new convolutional neural network (CNN) architecture for 2D driver/passenger
pose estimation and seat belt detection is proposed in this paper. The new
architecture is more nimble and thus more suitable for in-vehicle monitoring
tasks compared to other generic pose estimation algorithms. The new
architecture, named NADS-Net, utilizes the feature pyramid network (FPN)
backbone with multiple detection heads to achieve the optimal performance for
driver/passenger state detection tasks. The new architecture is validated on a
new data set containing video clips of 100 drivers in 50 driving sessions that
are collected for this study. The detection performance is analyzed under
different demographic, appearance, and illumination conditions. The results
presented in this paper may provide meaningful insights for the autonomous
driving research community and automotive industry for future algorithm
development and data collection.
