  The foreground segmentation algorithms suffer performance degradation in the
presence of various challenges such as dynamic backgrounds, and various
illumination conditions. To handle these challenges, we present a foreground
segmentation method, based on generative adversarial network (GAN). We aim to
segment foreground objects in the presence of two aforementioned major
challenges in background scenes in real environments. To address this problem,
our presented GAN model is trained on background image samples with dynamic
changes, after that for testing the GAN model has to generate the same
background sample as test sample with similar conditions via back-propagation
technique. The generated background sample is then subtracted from the given
test sample to segment foreground objects. The comparison of our proposed
method with five state-of-the-art methods highlights the strength of our
algorithm for foreground segmentation in the presence of challenging dynamic
background scenario.
