The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. This research leveraged work performed in the researchers' Department of Homeland Security (DHS) project by investigating the use of multiple cameras for monitoring human activities at critical transportation infrastructure sites. Methods dealt with detecting specified activities and counting humans in crowded scenes. Researchers further developed methods to automatically detect and spatially estimate an occlusion (common in crowded outdoor scenes) in world coordinates. The algorithms were tested at the transit stations where the DHS system was already deployed and covered activities that are not of interest to the DHS but were of major interest to Metro Transit (e.g., loitering, graffiti, drug dealing). The proposed methods, which focus on human activities, are directly applicable to a wide variety of transportation infrastructure sites.