SURFtogether: Towards Context Proximity Detection Using Visual Features

With the now near ubiquity of smart mobile devices and the advent of new wearable computing devices like Google Glass, context-aware computing applications are becoming more and more feasible. We propose a new concept coined Context-Proximity Awareness aimed at identifying closely related entities based on contextual similarity. As a first step towards that goal, we introduce the SURFtogether approach, trying to detect contextual proximity by analyzing the field of vision of two or more entities. We evaluate the general feasibility of our approach based on real-world data and show that in our initial tests, correct detection of contextual proximity is achieved nearly 90p of the time.

[1]  Bernd Girod,et al.  Comparison of local feature descriptors for mobile visual search , 2010, 2010 IEEE International Conference on Image Processing.

[2]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Axel Küpper,et al.  Efficient proximity and separation detection among mobile targets for supporting location-based community services , 2006, MOCO.

[4]  Peter A. Dinda,et al.  Indoor localization without infrastructure using the acoustic background spectrum , 2011, MobiSys '11.

[5]  Paolo Pirjanian,et al.  The vSLAM Algorithm for Robust Localization and Mapping , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Chadly Marouane,et al.  Indoor positioning using smartphone camera , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[7]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[8]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[9]  Anas Al-Nuaimi,et al.  Mobile Visual Location Recognition , 2013 .

[10]  Krystian Mikolajczyk,et al.  Evaluation of local detectors and descriptors for fast feature matching , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[11]  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).

[12]  Ig-Jae Kim,et al.  Indoor location sensing using geo-magnetism , 2011, MobiSys '11.

[13]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[14]  Tobias Höllerer,et al.  Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking , 2011, International Journal of Computer Vision.