  Due to the high volume of traffic on modern roadways, transportation agencies
have proposed High Occupancy Vehicle (HOV) lanes and High Occupancy Tolling
(HOT) lanes to promote car pooling. However, enforcement of the rules of these
lanes is currently performed by roadside enforcement officers using visual
observation. Manual roadside enforcement is known to be inefficient, costly,
potentially dangerous, and ultimately ineffective. Violation rates up to
50%-80% have been reported, while manual enforcement rates of less than 10% are
typical. Therefore, there is a need for automated vehicle occupancy detection
to support HOV/HOT lane enforcement. A key component of determining vehicle
occupancy is to determine whether or not the vehicle's front passenger seat is
occupied. In this paper, we examine two methods of determining vehicle front
seat occupancy using a near infrared (NIR) camera system pointed at the
vehicle's front windshield. The first method examines a state-of-the-art
deformable part model (DPM) based face detection system that is robust to
facial pose. The second method examines state-of- the-art local aggregation
based image classification using bag-of-visual-words (BOW) and Fisher vectors
(FV). A dataset of 3000 images was collected on a public roadway and is used to
perform the comparison. From these experiments it is clear that the image
classification approach is superior for this problem.
