Crowd-centric research is receiving increasingly more a ention as data sets on crowd behavior are becoming readily available. We have come to a point that 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 data sets. In this survey we concentrate exclusively on automatically gathering such data sets, 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 nd that much work is still needed, in particular when it comes to combing 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. CCS Concepts: •Information systems → Spatial-temporal systems; •Human-centered computing → Ubiquitous and mobile devices; •Computer systems organization → Sensors and actuators;

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We present Teamwork, an easy to use, portable system for team management. The distinguishing key feature of our solution is enhanced privacy provided by two means. First of all, all user content is moved from the cloud directly to users' devices, which share the data through a Peer-to-Peer overlay network for content distribution. Secondly, all content transferred through the network is secured in order to protect it from eavesdropping. Content is stored in files distributed through the Peer-to-Peer file system component named Teamshare.User's cognitive load is reduced by merging abstractions such as groups, organizations and projects into one simple concept -teams. The system is tightly integrated with existing mailing protocols, such that any task is an email.

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