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Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor

Published: 10 November 2019 Publication History

Abstract

Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.

References

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Cited By

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  • (2023)CarFi: Rider Side Localization using Wi-Fi CSI2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00072(530-538)Online publication date: 25-Sep-2023
  • (2022)Indoor occupancy estimation for smart utilities: A novel approach based on depth sensorsBuilding and Environment10.1016/j.buildenv.2022.109406222(109406)Online publication date: Aug-2022
  • (2021)Towards Silhouette-Aware Human Detection in Depth Images2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534347(1-8)Online publication date: 18-Jul-2021
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      cover image ACM Conferences
      DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis
      November 2019
      71 pages
      ISBN:9781450369930
      DOI:10.1145/3359427
      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 the author(s) 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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      New York, NY, United States

      Publication History

      Published: 10 November 2019

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      Author Tags

      1. Datasets
      2. depth data
      3. human detection
      4. occupancy estimation

      Qualifiers

      • Short-paper
      • Research
      • Refereed limited

      Funding Sources

      • U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) under the Building Technologies Office

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      Acceptance Rates

      DATA'19 Paper Acceptance Rate 16 of 21 submissions, 76%;
      Overall Acceptance Rate 74 of 167 submissions, 44%

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      Cited By

      View all
      • (2023)CarFi: Rider Side Localization using Wi-Fi CSI2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00072(530-538)Online publication date: 25-Sep-2023
      • (2022)Indoor occupancy estimation for smart utilities: A novel approach based on depth sensorsBuilding and Environment10.1016/j.buildenv.2022.109406222(109406)Online publication date: Aug-2022
      • (2021)Towards Silhouette-Aware Human Detection in Depth Images2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534347(1-8)Online publication date: 18-Jul-2021
      • (2020)Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI49375.2020.00016(92-103)Online publication date: Apr-2020
      • (2020)ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)10.1109/FG47880.2020.00135(801-808)Online publication date: Nov-2020

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