Smart robot perception through Internet data mining

This paper presents a high-level framework for smart robot perception to learn semantic concepts from videos that crawled from known Internet video sites (e.g. youtube, video-google). Smart perception is one of the most challenging problems for industrial robots. The key barrier for smart robot perception such as object detection and/or category classification is lack of annotated training data. Internet is a potential repository to provide a reliable source for semantic concept learning. In this paper, we will propose a novel Internet video-mining approach to bridge the gap between the demand of large-scale semantic concepts and the shortage of of annotated data. An automated video source discovery method will be addressed in concepts detection from the massive Internet videos. Illustrative experimental results with Tera-bytes level videos will be discussed to prove that the addressed method is effective and efficient in smart robot perception.