Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota
This report presents the formulation and implementation of an automated computer vision and machine learning
based system for estimation of the occupancy of passenger vehicles in high-occupancy vehicles and highoccupancy
toll (HOV/HOT) lanes. We employ a multi-modal approach involving near-infrared images and highresolution
color video images in conjunction with strong maximum margin based classifiers such as support vector
machines. We attempt to maximize the information that can be extracted from these two types of images by
computing different features. Then, we build classifiers for each type of feature which are compared to determine
the best feature for each imaging method. Based on the performance of the classifiers we critique the efficacy of
the individual approaches as the costs involved are significantly different.
Digital Technology Center, Department of Mechanical Engineering, University of Minnesota
Holec, Eric; Somasundaram, Guruprasad; Papanikolopoulos, Nikolaos; Morellas, Vassilios.
Monitoring the Use of HOV and HOT Lanes.
Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota.
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