  Wearing a seatbelt appropriately while driving can reduce serious
crash-related injuries or deaths by about half. However, current seatbelt
reminder system has multiple shortcomings, such as can be easily fooled by a
"Seatbelt Warning Stopper", and cannot recognize incorrect usages for example
seating in front of a buckled seatbelt or wearing a seatbelt under the arm.
General seatbelt usage recognition has many challenges, to name a few, lacking
of color information in Infrared (IR) cameras, strong distortion caused by wide
Field of View (FoV) fisheye lens, low contrast between belt and its background,
occlusions caused by hands or hair, and imaging blurry. In this paper, we
introduce a novel general seatbelt detection and usage recognition framework to
resolve the above challenges. Our method consists of three components: a local
predictor, a global assembler, and a shape modeling process. Our approach can
be applied to the driver in the Driver Monitoring System (DMS) or general
passengers in the Occupant Monitoring System (OMS) for various camera
modalities. Experiment results on both DMS and OMS are provided to demonstrate
the accuracy and robustness of the proposed approach.
