We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human-robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds.

This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under projects ERA-Net Chistera project ViSen PCIN-2013-047, RobInstruct TIN2014-58178-R, ROBOT-INT-COOP DPI2013-42458-P, and by the EU project AEROARMS H2020-ICT-2014-1-644271.

Peer Reviewed