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Poster Abstract: Intuitive Appliance Identification using Image Matching in Smart Buildings

Published: 04 November 2015 Publication History

Abstract

Identifying an appliance for interaction in commercial buildings becomes non-trivial as the number of smart appliances explodes. We present a system for users to intuitively "look up" appliances using image matching-based technique on a pre-constructed and annotated visual model of building interiors. It matched 98% images on a public robot-collected dataset and achieved 100% recall and precision. Our lab experiments with human captured videos and images also show the feasibility of real world deployments.

References

[1]
S. Dawson-Haggerty, X. Jiang, G. Tolle, J. Ortiz, and D. Culler. sMAP: a simple measurement and actuation profile for physical information. In SenSys. ACM, 2010.
[2]
M. Labbe and F. Michaud. Online global loop closure detection for large-scale multi-session graph-based slam. In IROS. IEEE, 2014.
[3]
B. Zhang, Y.-H. Chen, C. Tuna, A. Dave, Y. Li, E. Lee, and B. Hartmann. Hobs: head orientation-based selection in physical spaces. In SUI. ACM, 2014.

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Published In

cover image ACM Conferences
BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
November 2015
264 pages
ISBN:9781450339810
DOI:10.1145/2821650
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2015

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Author Tags

  1. identification
  2. image matching
  3. structure from motion

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  • National Science Foundation

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BuildSys '15 Paper Acceptance Rate 20 of 66 submissions, 30%;
Overall Acceptance Rate 148 of 500 submissions, 30%

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