  Recently, along with interest in autonomous vehicles, the importance of
monitoring systems for both drivers and passengers inside vehicles has been
increasing. This paper proposes a novel in-vehicle monitoring system the
combines 3D pose estimation, seat-belt segmentation, and seat-belt status
classification networks. Our system outputs various information necessary for
monitoring by accurately considering the data characteristics of the in-vehicle
environment. Specifically, the proposed 3D pose estimation directly estimates
the absolute coordinates of keypoints for a driver and passengers, and the
proposed seat-belt segmentation is implemented by applying a structure based on
the feature pyramid. In addition, we propose a classification task to
distinguish between normal and abnormal states of wearing a seat belt using
results that combine 3D pose estimation with seat-belt segmentation. These
tasks can be learned simultaneously and operate in real-time. Our method was
evaluated on a private dataset we newly created and annotated. The experimental
results show that our method has significantly high performance that can be
applied directly to real in-vehicle monitoring systems.
