Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
- Carnegie Mellon Univ., Pittsburgh, PA (United States); Carnegie Mellon University
- Bosch Research & Technology Center
- National University of Singapore
- Lawrence Berkeley National Laboratory
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
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), Building Technologies Office (EE-5B)
- Contributing Organization:
- Bosch Research & Technology Center National University of Singapore Lawrence Berkeley National Laboratory
- DOE Contract Number:
- EE0007682
- OSTI ID:
- 1576967
- Country of Publication:
- United States
- Language:
- English
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