The requirement of active localization techniques to attach a hardware device to the targets that need to be located can be difficult or even impossible for certain applications. For this reason, there has been an increasing interest in tagless or device-free localization (DFL) approaches. In particular, the research domain of RF-based device-free localization has been steadily evolving since its inception slightly over a decade ago. Many novel techniques have been developed regarding the three core aspects of DFL: detection, tracking, and identification. The increasing use of channel state information (CSI) has contributed considerably to these developments. In particular, the progress it enabled regarding the exceptionally difficult ‘identification problem’ has been highly impressive. In this survey, we provide a comprehensive overview of this evolutionary process, describe essential DFL concepts and highlight several key techniques whose creation marked important milestones within this field of research. We do so in a structured manner in which each technique is categorized according to the DFL core aspect it emphasizes most. Additionally, we discuss current blocking issues within the state-of-the-art and suggest multiple high-level research directions which will aid in the search towards eventual solutions.

The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis.

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