Economic and Synergistic Pedestrian Tracking System with Service Cooperation for Indoor Environments

This paper describes an indoor pedestrian tracking system that can economically improve the tracking performance and the quality and value of services by incorporating other services synergistically. The tracking system obtains position, orientation, and action of pedestrians continuously and accurately in large indoor environments by utilizing surveillance cameras and active RFID tags for security services and 3-D environment models for navigation services. Considering service cooperation and co-creative intelligence cycles, this system can improve both the tracking performance and the quality of services without significant increase of costs by sharing the existing infrastructures and the 3-D models among services. The authors conducted an evaluation of the tracking system in a large indoor environment and confirmed that the accuracy of the system can be improved by utilizing the infrastructures and the 3-D models. Synergistic services utilizing the tracking system and service cooperation can also enhance the quality and value of services.

[1]  Takeshi Kurata,et al.  Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System , 2006, ICAT.

[2]  David W. Murray,et al.  Video-rate localization in multiple maps for wearable augmented reality , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[3]  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).

[4]  Oliver Bimber,et al.  Enabling Mobile Phones To Support Large-Scale Museum Guidance , 2007, IEEE MultiMedia.

[5]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[6]  T. Okuma,et al.  A pilot user study on 3-D museum guide with route recommendation using a sustainable positioning system , 2007, 2007 International Conference on Control, Automation and Systems.

[7]  Dieter Schmalstieg,et al.  Experiences with Handheld Augmented Reality , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[8]  Neil J. Gordon,et al.  Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter , 1993 .

[9]  Robert Harle,et al.  Pedestrian localisation for indoor environments , 2008, UbiComp.

[10]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[11]  Tomoya Ishikawa,et al.  In-Situ 3D Indoor Modeler with a Camera and Self-contained Sensors , 2009, HCI.