Neural Network Control for Active Cameras Using Master-Slave Setup

Renan Oliveira Reis, Igor Dias, William Robson Schwartz

Abstract: The use of active cameras has increased to perform tasks such as tracking and biometrics at distance. Furthermore, recent efforts have focused on the master-slave setup, which is composed of fixed and PTZ cameras. Although, there are many works regarding active camera control, there is no standard way to compare different control approaches once the experiment cannot be reproduced. Thus, in this work, besides the proposition of a novel learning-based approach to the master-slave setup, we also propose an experimental setup that allows a fair comparison between different methods. The proposed control method learns corresponding points between the fixed and the PTZ cameras using a neural network. The novel experimental setup places two PTZ cameras side-by-side with a very similar view so that two different algorithms can be executed simultaneously. The experiments show that the proposed method is better than literature method when the focus is centralizing a target at the PTZ view.