Moving Object Segmentation

Moving object segmentation (MOS) aims to identify and delineate moving objects within a scene, a crucial task for applications like autonomous driving and robotics. Current research heavily focuses on leveraging spatio-temporal information from various sensor modalities (LiDAR, radar, cameras) using deep learning architectures, including transformer networks, convolutional neural networks, and graph neural networks, often incorporating multi-view fusion and memory mechanisms to improve accuracy and robustness. These advancements are significantly impacting the development of safer and more efficient autonomous systems and are also finding applications in medical image analysis.