Publication
Interactive multiple object learning with scanty human supervision
Journal Article (2016)
Journal
Computer Vision and Image Understanding
Pages
51-64
Volume
149
Doc link
http://dx.doi.org/10.1016/j.cviu.2016.03.010
File
Authors
Projects associated
ViSen: Visual Sense, Tagging visual data with semantic descriptions
Robot-Int-Coop: Robot-Human Interaction, Cooperation and Learning in Urban Areas
AEROARMS: AErial RObotics System integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance
RobInstruct: Instructing robots using natural communication skills
Abstract
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.
Categories
computer vision, object detection.
Author keywords
Object recognition; Interactive learning; Online classifier; Human-robot interaction
Scientific reference
M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Interactive multiple object learning with scanty human supervision. Computer Vision and Image Understanding, 149: 51-64, 2016.
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