Background Subtraction

Background subtraction, a core computer vision task, aims to isolate moving objects from static backgrounds in video sequences. Current research emphasizes robust algorithms that handle challenging scenarios like dynamic backgrounds, shadows, and camera jitter, often employing unsupervised deep learning methods such as generative neural networks, robust principal component analysis (RPCA), and graph convolutional networks (GCNs). These advancements improve accuracy and efficiency, particularly in real-time applications, and are evaluated on benchmark datasets like CDnet 2014. The resulting foreground segmentation has broad applications in video surveillance, object tracking, and human-computer interaction.