Demo: Fusing WiFi and Video Sensing for Accurate Group Detection in Indoor Spaces

Understanding one's group context in indoor spaces is useful for many reasons -- e.g., at a shopping mall, knowing a customer's group context can help in offering context-specific incentives, or estimating taxi demand for customers exiting the mall. We presented GruMon in Sen et. al, which detects groups accurately under accurate localization assumptions or with the availability of inertial sensors from smartphones carried by the users. However, in most real situations, (1) client-side sensory information is not available, and (2) server-side localization is erroneous. Further, detecting people who not carry mobile phones with them (e.g., children/elders and phones with WiFi turned OFF, which we refer to as ``hidden nodes"), and separating out smaller sub-groups from a larger group that happens to share similar trajectories, remains a challenge. In this demo paper, we present our improved system for group detection in indoor environments. We overcome the key challenges of localization errors, presence of hidden nodes and sub-groups by fusing the WiFi and video sensing modalities.

[1]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Bodhi Priyantha,et al.  The Cricket indoor location system , 2005 .

[3]  Archan Misra,et al.  Need accurate user behaviour?: pay attention to groups! , 2015, UbiComp.

[4]  Kyu-Han Kim,et al.  SAIL: single access point-based indoor localization , 2014, MobiSys.

[5]  Yuan F. Zheng,et al.  E-V: Efficient visual surveillance with electronic footprints , 2012, 2012 Proceedings IEEE INFOCOM.

[6]  Swati Rallapalli,et al.  Physical Analytics: A New Frontier for (Indoor) Location Research , 2013 .

[7]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  B. S. Manjunath,et al.  Entity reconciliation in a multi-camera network , 2016, ICDCN.

[9]  Azriel Rosenfeld,et al.  Tracking Groups of People , 2000, Comput. Vis. Image Underst..

[10]  Bernt Schiele,et al.  Detection and Tracking of Occluded People , 2014, International Journal of Computer Vision.

[11]  Kiyoharu Aizawa,et al.  Tracking Persons using Particle Filter Fusing Visual and Wi-Fi Localizations for Widely Distributed Camera , 2007, 2007 IEEE International Conference on Image Processing.

[12]  Archan Misra,et al.  Event detection: Exploiting socio-physical interactions in physical spaces , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[13]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[16]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[17]  Archan Misra,et al.  GruMon: fast and accurate group monitoring for heterogeneous urban spaces , 2014, SenSys.