Unsupervised Background Subtraction Using Generator-Discriminator Learning

Background subtraction is a core problem in computer vision, widely used in video surveillance to segment moving foreground objects from video sequences. While deep learning approaches have shown strong performance - especially under dynamic backgrounds and sudden illumination changes - they typically rely on large-scale, high-quality labeled video datasets. Acquiring such data is time-consuming and expensive, making existing supervised or weakly supervised methods less suitable for real-time applications. Moreover, many of these methods suffer from performance degradation when applied to unseen video sequences. To address these challenges, we present UTGMP-BS algorithm: an Unsupervised Transformer-based pseudo-label Generator with a Message-Passing network for the Background Subtraction task. UTGMP-BS is a fully unsupervised framework designed to learn directly from unlabeled video sequences. It comprises two key components: a transformer-based pseudo-label generator, which produces initial pixel-level foreground and background labels using an encoder-decoder architecture and an L₁ loss, and a message-passing network, which acts as a label-cleaner discriminator to refine the pseudo labels and enforce spatial consistency. These two branches engage in mutual learning through consecutive iterations, enhancing one another's performance without any ground-truth supervision. The framework is trained using an alternating iterative learning strategy with binary cross-entropy loss, achieving robust background subtraction across varied scenes. Extensive experiments on six publicly available benchmark datasets demonstrate that UTGMP-BS achieves competitive results compared to existing State-of-The-Art (SOTA) methods.