To manage ever increasing traffic volume on modern highways, transportation agencies have introduced special managed lanes where only vehicles with a certain occupancy level are allowed. This encourages highway users to ride together, thus, in theory, more efficiently transporting people through the highway system. In order to be effective, however, adherence to the vehicle occupancy rules has to be enforced. Recent studies have shown that the traditional approach of dispatching traffic law enforcement officers to perform roadside visual inspections is not only expensive and dangerous, but also ineffective for managed lane enforcement. In this paper, we describe an image-based machine learning approach for automatic or semi-automatic vehicle occupancy detection. Our method localizes windshield regions by constructing an elastic deformation model from sets of uniquely defined landmark points along the front windshield. From the localized windshield region, the method calculates image-level feature representations, which are then applied to a trained classifier for classifying the vehicle into violator and non-violator classes.