Reducing adaptation latency for multi-concept visual perception in outdoor environments

Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by lab...

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Main Authors: WIGNESS, Maggie, ROGERS, John G., NAVARRO-SERMENT, Luis Ernesto, SUPPE, Arne, DRAPER, Bruce A.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/8235
https://ink.library.smu.edu.sg/context/sis_research/article/9238/viewcontent/wigness_iros16.pdf
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spelling sg-smu-ink.sis_research-92382023-10-26T03:28:49Z Reducing adaptation latency for multi-concept visual perception in outdoor environments WIGNESS, Maggie ROGERS, John G. NAVARRO-SERMENT, Luis Ernesto SUPPE, Arne DRAPER, Bruce A. Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires new or additional labeling to adapt visual perception. The time is takes for a human to label new data is what we call adaptation latency. High adaptation latency is not simply undesirable but may be infeasible for scenarios with limited labeling time and resources. In this paper, we introduce a labeling framework to the environment perception domain that significantly reduces adaptation latency using unsupervised learning in exchange for a small amount of label noise. Using two real-world datasets we demonstrate the speed of our labeling framework, and its ability to collect environment labels that train high performing multi-concept classifiers. Finally, we demonstrate the relevance of this label collection process for visual perception as it applies to navigation in outdoor environments. 2016-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8235 info:doi/10.1109/IROS.2016.7759432 https://ink.library.smu.edu.sg/context/sis_research/article/9238/viewcontent/wigness_iros16.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
spellingShingle Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
WIGNESS, Maggie
ROGERS, John G.
NAVARRO-SERMENT, Luis Ernesto
SUPPE, Arne
DRAPER, Bruce A.
Reducing adaptation latency for multi-concept visual perception in outdoor environments
description Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires new or additional labeling to adapt visual perception. The time is takes for a human to label new data is what we call adaptation latency. High adaptation latency is not simply undesirable but may be infeasible for scenarios with limited labeling time and resources. In this paper, we introduce a labeling framework to the environment perception domain that significantly reduces adaptation latency using unsupervised learning in exchange for a small amount of label noise. Using two real-world datasets we demonstrate the speed of our labeling framework, and its ability to collect environment labels that train high performing multi-concept classifiers. Finally, we demonstrate the relevance of this label collection process for visual perception as it applies to navigation in outdoor environments.
format text
author WIGNESS, Maggie
ROGERS, John G.
NAVARRO-SERMENT, Luis Ernesto
SUPPE, Arne
DRAPER, Bruce A.
author_facet WIGNESS, Maggie
ROGERS, John G.
NAVARRO-SERMENT, Luis Ernesto
SUPPE, Arne
DRAPER, Bruce A.
author_sort WIGNESS, Maggie
title Reducing adaptation latency for multi-concept visual perception in outdoor environments
title_short Reducing adaptation latency for multi-concept visual perception in outdoor environments
title_full Reducing adaptation latency for multi-concept visual perception in outdoor environments
title_fullStr Reducing adaptation latency for multi-concept visual perception in outdoor environments
title_full_unstemmed Reducing adaptation latency for multi-concept visual perception in outdoor environments
title_sort reducing adaptation latency for multi-concept visual perception in outdoor environments
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/8235
https://ink.library.smu.edu.sg/context/sis_research/article/9238/viewcontent/wigness_iros16.pdf
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