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Authors: Andreas Hofmann 1 and Paul Robertson 2

Affiliations: 1 Dynamic Object Language Labs, Inc. and Massachusetts Institute of Technology, United States ; 2 Dynamic Object Language Labs and Inc., United States

ISBN: 978-989-758-090-1

Keyword(s): Active Perception, POMDP, Belief State Planning.

Related Ontology Subjects/Areas/Topics: Active and Robot Vision ; Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Mobile Imaging ; Motion, Tracking and Stereo Vision ; Pervasive Smart Cameras

Abstract: Existing machine perception systems are too inflexible, and therefore cannot adapt well to environment uncertainty. We address this problem through a more dynamic approach in which reasoning about context is used to actively and effectively allocate and focus sensing and action resources. This Active Perception approach prioritizes the system’s overall goals, so that perception and situation awareness are well integrated with actions to focus all efforts on these goals in an optimal manner. We use a POMDP (Partially Observable Markov Decision Process) framework, but do not attempt to compute a comprehensive control policy, as this is intractible for practical problems. Instead, we employ Belief State Planning to compute point solutions from an initial state to a goal state set. This approach automatically generates action sequences for sensing operations that reduce uncertainty in the belief state, and ultimately achieve the goal state set.

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Paper citation in several formats:
Hofmann, A. and Robertson, P. (2015). Active Perception - Improving Perception Robustness by Reasoning about Context.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 328-336. DOI: 10.5220/0005298103280336

@conference{visapp15,
author={Andreas Hofmann. and Paul Robertson.},
title={Active Perception - Improving Perception Robustness by Reasoning about Context},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={328-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005298103280336},
isbn={978-989-758-090-1},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Active Perception - Improving Perception Robustness by Reasoning about Context
SN - 978-989-758-090-1
AU - Hofmann, A.
AU - Robertson, P.
PY - 2015
SP - 328
EP - 336
DO - 10.5220/0005298103280336

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