Occlusion-capable research refers to the study or development of technologies, algorithms, or systems that are capable of detecting, identifying, and handling occlusions in visual perception tasks. Occlusions occur when objects or elements in a scene are partially or completely obstructed by other objects, creating challenges for computer vision systems in accurately interpreting and understanding the scene. Research in occlusion-capable technology aims to improve the robustness and accuracy of computer vision systems in various applications, such as object recognition, tracking, and scene understanding, by effectively handling occlusions. Techniques used in this area may include deep learning, neural networks, and computational geometry to address occlusion-related challenges.