Knowledge-based Visual Context-Aware Framework for Applications in Robotic Services

Recently, context awareness in vision technologies has become essential with the increasing demand for realworld applications, such as surveillance systems and service robots. However, implementing context awareness with an end-to-end learning-based system limits its extensibility and performance because the context varies in scope and type, but related data are mostly rare. To mitigate these limitations, we propose a visual context-aware framework composed of independent processes of visual perception and context inference. The framework performs logical inferences using the abstracted visual information of recognized objects and relationships based on our knowledge representation. We demonstrate the scalability and utility of the proposed framework through experimental cases that present stepwise context inferences applied to robotic services in different domains.