CoenoSense: A Framework for Real-Time Detection and Visualization of Collective Behaviors in Human Crowds by Tracking Mobile Devices
There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we present a framework to infer and visualize crowd behavior patterns in real-time from pedestrians’ GPS location traces. We deployed and tested our framework during the 2011 Lord Mayor’s Show in London. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device’s location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. We learned from the police officers that our framework helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
KeywordsMobile Phone Location Update Crowd Condition Crowd Behavior Crowd Density
This work is supported under the FP7 ICT Future Enabling Technologies Programme under grant agreement No. 231288 (SOCIONICAL).
- 5.Johansson A, Helbing D, Al-Abideen HZ, Al-Bosta S (2008) From crowd dynamics to crowd safety: a video-based analysis. Adv Complex Syst 11:479–527 Google Scholar
- 7.Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE computer vision and pattern recognition Google Scholar
- 10.Song B, Sethi RJ, Roy-Chowdhury AK (2011) Wide area tracking in single and multiple views. In: Visual analysis of humans. Springer, London Google Scholar
- 11.Kim D, Kim Y, Estrin D, Srivastava M (2010) Sensloc: sensing everyday places and paths using less energy. In: Proc of the 8th ACM conference on embedded networked sensor systems. ACM, New York Google Scholar
- 12.Wirz M, Roggen D, Troster G (2010) User acceptance study of a mobile system for assistance during emergency situations at large-scale events. In: 3rd international conference on human-centric computing (HumanCom 2010). IEEE Press, New York Google Scholar