Advertisement

Place Recognition Using Multiple Wearable Cameras

  • Kyungmin Min
  • Seonghun Lee
  • Kee-Eung Kim
  • Jin Hyung Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4836)

Abstract

Recognizing a user’s location is the most challenging problem for providing intelligent location-based services. In this paper, we presented a real-time camera-based system for the place recognition problem. This system takes streams of scene images of a learned environment from user-worn cameras and produces the class label of the current place as an output. Multiple cameras are used to collect multi-directional scene images because utilizing multiple images yields better and robust recognition than a single image. For more robust recognition, we utilized spatial relationships between the places. In addition that, a temporal reasoning is incorporated with a Markov model to reflect typical staying time at each place. Recognition experiments, which were conducted in a real environment in a university campus, showed that the proposed method yields a very promising result.

Keywords

context recognition place recognition image understanding wearable computing hidden Markov models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Loomis, J.M.R., Golledge, R.G., Klatzky, R.L., Speigle, J.M., Tietz, J.: Personal guidance system for the visually impaired. In: 1st annual ACM conference on Assistive Technologies, pp. 85–90. ACM Press, New York (1994)CrossRefGoogle Scholar
  2. 2.
    Rhodes, B., Starner, T.: Remembrance agent: a continuously running automated information retrieval system. In: 1st International Conference on the Practical Application of Intelligent Agents and Multi Agent Technology, pp. 487–495 (1996)Google Scholar
  3. 3.
    Clarkson, B., Mase, K., Pentland, A.: Recognizing User Context via Wearable Sensors. In: 4th IEEE International Symposium on Wearable Computers, pp. 69–74. IEEE Press, Los Alamitos (2000)Google Scholar
  4. 4.
    Lee, S., Mase, K.: Activity and Location Recognition Using Wearable Sensors. Pervasive Computing 1(3), 24–32 (2002)CrossRefGoogle Scholar
  5. 5.
    Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: 9th IEEE Int’l Conf. on Computer Vision, vol. 1, pp. 273–280. IEEE Press, Los Alamitos (2003)Google Scholar
  6. 6.
    Li, F., Kosecka, J.: Probabilistic Location Recognition using Reduced Feature Set. In: IEEE Int. Conf. on Robotics and Automation, pp. 3405–3410. IEEE Press, Los Alamitos (2006)Google Scholar
  7. 7.
    Simoncelli, E.P., Freeman, W.T.: The steerable pyramid: a flexible architecture for multi-scale derivative computation. In: IEEE Int. Conf. on Image Processing, vol. 3, pp. 444–447. IEEE Press, Los Alamitos (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kyungmin Min
    • 1
  • Seonghun Lee
    • 1
  • Kee-Eung Kim
    • 1
  • Jin Hyung Kim
    • 1
  1. 1.Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, DaejeonKorea

Personalised recommendations