Abstract
Video surveillance systems are commonly designed based on background estimation and subtraction techniques to support further object detection and tracking procedures. In this paper, a new kernel-based adaptive learning approach, called Correntropy-based Adaptive Learning - CAL, is proposed to deal with stationary and non-stationary pixel dynamics to support video-based surveillance systems. To this end, an stochastic gradient based adaptive learning algorithm is enhanced using Correntropy cost function to learn, in a dynamic way, a background model that allows diminishing influence of intrinsic video artifacts. Moreover, automatic estimation of Correntropy kernel bandwidth value is carried out by means of shape variations of the CAL error distribution. Obtained results show that the CAL algorithm outperforms, in well-known real-world datasets, state of the art methodologies, suitably modeling the scene background with acceptable computational burden.