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

Conference · Sun Nov 10 00:00:00 EST 2019 · DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis

DOI:https://doi.org/10.1145/3359427.3361916· OSTI ID:1577344

Flores, Fabricio  ^[1]; Munir, Sirajum  ^[2]; Quintana, Matias  ^[3]; Krishnan Prakash, Anand  ^[4]; Berges, Mario  ^[1]

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1. Carnegie Mellon University
2. Bosch Research & Technology Center
3. National University of Singapore
4. Lawrence Berkeley National

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.

• Research Organization: Carnegie Mellon Univ., Pittsburgh, PA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• DOE Contract Number: EE0007682
• OSTI ID: 1577344
• Journal Information: DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis, Conference: SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems , New York, NY, USA, November 10, 2019
• Country of Publication: United States
• Language: English

Related Subjects

47 OTHER INSTRUMENTATION
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
Datasets
depth data
human detection
occupancy estimation