Background subtraction using Artificial Immune Recognition System and Single Gaussian (AIRS-SG)

Background subtraction is an essential step in the video monitoring process. Several models have been proposed to differentiate background pixels from foreground pixels. However, most of these methods fail to distinguish them in highly dynamic environments. In this paper, we propose a new method robust and more efficient for distinguishing moving objects from static objects in dynamic scenes. For this purpose, we propose to use a bio-inspired approach based on the Artificial Immune Recognition System (AIRS) as a classification tool. AIRS separates antibodies, represented by the pixels of the background model, from the antigens that model foreground pixels representing moving objects. Each pixel is modeled by a feature vector containing the attributes of a Gaussian. Only the pixels classified as background are taken into account by the system and updated in the model. This combination has allowed to benefit from two advantages: the power of AIRS to provide an online update of system parameters and the ability of Gaussians to adapt to scene variations at the pixel level. To test the proposed approach, six videos representing the dynamic background category of the CDnet 2014 dataset are selected. Obtained results proved the effectiveness of this new process in terms of quality and complexity compared to other state-of-the-art methods.