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Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through(More)
Recent technological advances foster the spreading of social software in the mobile domain. Hence, future usage patterns of mobile devices will involve more group interaction. While collaboration using mobile devices is an active area of research, only limited attention has been paid to the efficient initiation of group communication from mobile terminals.(More)
Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. This paper addresses the joint identification of the indoor movement of <i>multiple</i> persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features derived from multiple sensor modalities of modern(More)
We propose an approach for detecting collectivebehavior patterns using body-worn sensors in a decentralized,online manner. To reduce complexity, we introduce a set ofcollective behavior primitives as building blocks, adequate fordetection by sensing and communications means. With this, wepresent a generic distributed signal processing procedure suitablefor(More)
The vast availability of mobile phones with built-in movement and location sensors enable the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper(More)
In Pervasive Computing research, substantial work has been directed towards radio-based sensing of human movement patterns. This research has, however, mainly been focused on movements of individuals. This paper addresses the joint identification of the movement indoors of multiple persons forming a cohesive whole - specifically flocks - with clustering(More)
Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location trajectories. Compared to prior work, our method provides increased robustness against the influence of(More)
Methods for recognizing group affiliations using mobile devices have been proposed using centralized instances to aggregate and evaluate data. However centralized systems do not scale well and fail when the network is congested. We present a method for distributed, peer-to-peer (P2P) recognition of group affiliations in multi-group environments, using the(More)
City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations.(More)