Silhouette classification by using manifold learning for automated threat detection

Video surveillance systems have become an essential tool to enhance security in both public and private places, especially to prevent potentially dangerous situations. However, these systems usually have a high number of nuisance alarms, when they are aimed at detecting automatically abandoned objects. It was found that people waiting (sitting or standing still) in airports, train stations and bus stops are the main cause of false alarms, as available video surveillance technologies are not focused on recognizing the abandoned objects. In this paper, we present a methodology to recognize abandoned objects. The goal is to determinate if the alarm is caused by an unattended baggage or a stationery person, as the former may pose potential security threats. The R transform, which is a geometric invariant feature descriptor and has low computational complexity, is applied to each of the four patches in which the silhouette of the object to be recognized is divided. Afterwards a covariance matrix representation is calculated from both the original high dimensional space and a low dimensional space obtained with Laplacian Egienmaps, being this matrix a point in a Riemannian Manifold. The proposed methodology is evaluated in a single person dataset and a baggage dataset (gathered from the web) and good performance was obtained.