What epipolar geometry can do for video-surveillance

Abstract

In this paper we deal with the problem of matching moving objects between multiple views using geometrical constraints. We consider systems of still, uncalibrated and partially overlapped cameras and design a method able to automatically learn the epipolar geometry of the scene. The matching step is based on a functional that computes the similarity between objects pairs jointly considering different contributions from the geometry. We obtain an efficient method for multi-view matching based on simple geometric tools, requiring a very limited human intervention, and characterized by a low computational load. We will discuss the potential of our approach for video-surveillance applications on real data, showing very good results. Also, we provide an example of application to the consistent labeling problem for multi-camera tracking, and report a comparative analysis with other methods from the state of the art on the PETS 2009 benchmark dataset. © 2013 Springer-Verlag.