Neural controller for PTZ cameras based on nonpanoramic foreground detection
Miguel A. Molina‐Cabello, Ezequiel López‐Rubio, Rafael Marcos Luque‐Baena, Enrique Domínguez, Karl Thurnhofer‐Hemsi · 2017
In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a non-parametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.