Background Estimation

Background estimation aims to separate foreground objects of interest from their surrounding context, a crucial preprocessing step in many computer vision tasks. Current research focuses on improving robustness to dynamic backgrounds and variations in scene complexity, employing techniques like generative neural networks, residual modeling, and adaptive background models within deep learning frameworks such as CNNs and Transformers. These advancements are vital for enhancing the accuracy and reliability of applications ranging from gas plume detection in hyperspectral imagery to improved semantic segmentation in class-incremental learning and robust object recognition in challenging environments. The development of more accurate and adaptable background estimation methods directly impacts the performance of numerous downstream applications.