ROST: Realtime Online Spatiotemporal Topics
Yogesh Girdhar , PhD Candidate, McGill Center for Intelligent Machines

Area: computer vision and robotics

Abstract:

We describe a novel online topic modeling framework to compute a low dimension descriptor of visual observations made by a mobile robot, which is sensitive to the structural and thematic changes in the environment. Our approach is designed to run in realtime, and is suitable for long term execution on a robotic platform. Using this image descriptor we build online anytime summaries consisting of surprising observations experienced by a robot thus far. The observations in the summary are chosen such that they cover the set of all observations in topic space, while minimizing the cover radius. Like almost any summarization method, the technique is meant to produce data for human consumption. Thus, we assess our approach on 307 human subjects and compare it to the classic bag-of-words description based summaries, and find it superior.