A method of camera selection based on partially observable Markov decision process model in camera networks

Camera selection in camera networks is a dynamic decision-making process based on the analysis and evaluation of visual content. In this paper, a novel camera selection method based on a partially observable Markov decision process model (POMDP) is proposed, in which the belief states of the model are used to represent noisy visual information and an innovative evaluation function is defined to identify the most informative of several multi-view video streams. Our experiments show that these proposed visual evaluation criteria successfully measure changes in scenes and our camera selection method effectively reduces camera switching when compared to other state-of-the-art methods.