Monitoring the Use of HOV and HOT Lanes

TL;DRAbstract

This report presents the formulation and implementation of an automated computer vision and machine learning
\nbased system for estimation of the occupancy of passenger vehicles in high-occupancy vehicles and highoccupancy
\ntoll (HOV/HOT) lanes. We employ a multi-modal approach involving near-infrared images and highresolution
\ncolor video images in conjunction with strong maximum margin based classifiers such as support vector
\nmachines. We attempt to maximize the information that can be extracted from these two types of images by
\ncomputing different features. Then, we build classifiers for each type of feature which are compared to determine
\nthe best feature for each imaging method. Based on the performance of the classifiers we critique the efficacy of
\nthe individual approaches as the costs involved are significantly different.

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