Crowd-centric research is receiving increasingly more attention as datasets on crowd behavior are becoming readily available. We have come to a point where many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world datasets. In this survey, we concentrate exclusively on automatically gathering such datasets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we find that much work is still needed, in particular when it comes to combining privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior.