Long term occupancy estimation in a commercial space: an empirical study
Author:
Affiliation:
1. Stony Brook University
2. Bosch Research and Technology Center
Funder
U.S. Department of Energy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3055031.3055062
Reference4 articles.
1. EIA. http://www.eia.doe.gov/. EIA. http://www.eia.doe.gov/.
2. S. Munir R. S. Arora C. Hesling J. Li J. Francis C. Shelton C. Martin A. Rowe and M. Berges. 2017. Real-Time Fine Grained Occupancy Estimation using Depth Sensors on ARM Embedded Platforms. In RTAS. S. Munir R. S. Arora C. Hesling J. Li J. Francis C. Shelton C. Martin A. Rowe and M. Berges. 2017. Real-Time Fine Grained Occupancy Estimation using Depth Sensors on ARM Embedded Platforms. In RTAS.
3. The MERL motion detector dataset
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