Crowd-Centric Counting via Unsupervised Learning
Name
CCC-via-UL-VFE.pdf
Description
Accepted version
Size
2.74 MB
Format
Adobe PDF
Checksum (MD5)
6acf481afcd01d5fde78281d736e9fb1
Author(s)
Morselli, Flavio
Bartoletti, Stefania
Mazuelas, Santiago
Win, Moe Z.
Conti, Andrea
Date Issued
May 2019
Journal
2019 IEEE International Conference on Communications Workshops (ICC Workshops) : proceedings : Shanghai, China, 22-24 May 2019
Publisher
IEEE
Citation
Morselli, Flavio et al. "Crowd-Centric Counting via Unsupervised Learning." 2019 IEEE International Conference on Communications Workshops (ICC Workshops) : proceedings : Shanghai, China, 22-24 May 2019, IEEE, 2019
Version
Author's final manuscript
Abstract
Counting targets (people or things) within a monitored area is an important task in emerging wireless applications, including those for smart environments, safety, and security. Conventional device-free radio-based systems for counting targets rely on localization and data association (i.e., individual-centric information) to infer the number of targets present in an area (i.e., crowd-centric information). However, many applications (e.g., affluence analytics) require only crowd-centric rather than individual-centric information. Moreover, individual-centric approaches may be inadequate due to the complexity of data association. This paper proposes a new technique for crowd-centric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. The proposed technique is validated via experimentation using an ultra-wideband sensor radar in an indoor environment.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccw.2019.8757112
