Crowd-Centric Counting via Unsupervised Learning

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.