Camera-based visibility estimation: Incorporating multiple regions and unlabeled observations

The major contributions of this paper are twofold. First, we propose two predictive models that incorporate multiple scene regions into the estimation: regression trees and multivariate linear regression. Incorporating multiple regions is important since regions at different distances are effective for estimating light extinction under different visibility regimes. The second major contribution is a semi-supervised learning framework, which incorporates unlabeled training samples to improve the learned models. Leveraging unlabeled data for learning is important since in many applications, it is easier to obtain observations than to label them. We evaluate our models using a dataset of images and ground truth light extinction values from a visibility camera system in Phoenix, Arizona.