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The foreground segmentation algorithms suffer performance degradation in thepresence of various challenges such as dynamic backgrounds, and variousillumination conditions. To handle these challenges, we present a foregroundsegmentation method, based on generative adversarial network (GAN). We aim tosegment foreground objects in the presence of two aforementioned majorchallenges in background scenes in real environments. To address this problem,our presented GAN model is trained on background image samples with dynamicchanges, after that for testing the GAN model has to generate the samebackground sample as test sample with similar conditions via back-propagationtechnique. The generated background sample is then subtracted from the giventest sample to segment foreground objects. The comparison of our proposedmethod with five state-of-the-art methods highlights the strength of ouralgorithm for foreground segmentation in the presence of challenging dynamicbackground scenario.

Illumination Invariant Foreground Object Segmentation using ForeGANs