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Viewpoint detection models for sequential embodied object category recognition

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3 Author(s)
David Meger ; Department of Computer Science, University of British Columbia, Vancouver, Canada, V6T 1Z4 ; Ankur Gupta ; James J. Little

This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning. We have examined such models for several state-of-the-art object detection methods. Our learning procedure has been evaluated using an exhaustive multiview category database recently collected for multiview category recognition research. Our approach has been evaluated on a simulator that is based on real images that have previously been collected. Simulation results verify that our viewpoint planning approach requires fewer viewpoints for confident recognition. Finally, we illustrate the applicability of our method as a component of a completely autonomous visual recognition platform that has previously been demonstrated in an object category recognition competition.

Published in:

Robotics and Automation (ICRA), 2010 IEEE International Conference on

Date of Conference:

3-7 May 2010