Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods

Abstract

Background-Foreground separation and appearance generation is a fundamental step in many computer vision applications. Existing methods like Robust Subspace Learning (RSL) suffer performance degradation in the presence of challenges like bad weather, illumination variations, occlusion, dynamic backgrounds and intermittent object motion. In the current work we propose a more accurate deep neural network based model for background-foreground separation and complete appearance generation of the foreground objects. Our proposed model, Guided Attention based Adversarial Model (GAAM), can efficiently extract pixel-level boundaries of the foreground objects for improved appearance generation. Unlike RSL methods our model extracts the binary information of foreground objects labeled as attention map which guides our generator network to segment the foreground objects from the complex background information. Wide range of experiments performed on the benchmark CDnet2014 dataset demonstrate the excellent performance of our proposed model.