2019/02/07 by M. Sultana, Sultana, Maryam, Soon Ki Jung +1
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1902.03120
openalex publication_date 2019/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.