Crowd-Centric Counting via Unsupervised Learning
Author(s)Morselli, Flavio; Bartoletti, Stefania; Mazuelas, Santiago; Win, Moe Z.; Conti, Andrea
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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.
DepartmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
2019 IEEE International Conference on Communications Workshops (ICC Workshops) : proceedings : Shanghai, China, 22-24 May 2019
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
Author's final manuscript