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WinSet: the first multi-modal window dataset for heterogeneous window states

Authors:

Cheng-Hsin Hsu,

Nalini VenkatasubramanianAuthors Info & Claims

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 192 - 195

https://doi.org/10.1145/3486611.3486662

Published: 17 November 2021 Publication History

Abstract

Windows play an important role in modern buildings. Getting to know the window states, e.g., open vs. close, is an enabler of many smart city applications, such as energy conservation and emergency response. In this work, we collect the very first multi-modal (RGB, thermal, depth, LiDAR, and ultrasound) window dataset named WinSet at various distances and angles. Multiple window types and heterogeneous window states are considered, such as openness (open vs. close), human behind (with vs. without), and lighting (on vs. off). Although our WinSet dataset has many usage scenarios, we concretize two sample ones: (i) analysis of state distinguishability using different sensor modalities and (ii) algorithms to detect open windows. We believe sharing WinSet and its collection procedure with the engineering and research communities will stimulate many creative smart city applications.

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Cited By

Ramani VMartin MArjunan PChong APoolla KMiller C(2023)Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterizationEnergy and Buildings10.1016/j.enbuild.2023.112997287(112997)Online publication date: May-2023
https://doi.org/10.1016/j.enbuild.2023.112997

Index Terms

WinSet: the first multi-modal window dataset for heterogeneous window states
1. Information systems
  1. Information systems applications
    1. Multimedia information systems

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Published In

cover image ACM Conferences

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2021

388 pages

ISBN:9781450391146

DOI:10.1145/3486611

General Chair:
Xiaofan (Fred) Jiang
Columbia University
,
Program Chairs:
Omprakash Gnawali
University of Houston
,
Zoltan Nagy
The University of Texas Austin

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2021

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Short-paper

Funding Sources

Defense Advanced Research Projects Agency
Ministry of Science and Technology (Taiwan)
UC Office of the President

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BuildSys '21

Sponsor:

SIGEnergy

BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 17 - 18, 2021

Coimbra, Portugal

Acceptance Rates

BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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SenSys '24

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The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

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Cited By

Ramani VMartin MArjunan PChong APoolla KMiller C(2023)Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterizationEnergy and Buildings10.1016/j.enbuild.2023.112997287(112997)Online publication date: May-2023
https://doi.org/10.1016/j.enbuild.2023.112997

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