Panorama construction for PTZ camera surveillance with the neural gas network

Abstract

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on the neural gas network and a subsequent piecewise linear interpolation by Delaunay triangulation. Furthermore, an ensemble model of neural gas networks is also proposed. The approach can handle arbitrary camera directions and zooms for a pan‐tilt‐zoom camera‐based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed methods are effective and suitable to use for real‐time video surveillance applications.