A Portable and Low-Cost E-Learning Video Capture System

  • Richard Y. D. Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


In the recent times, many computer vision supported e-learning applications have been constructed, to provide the participants with the automated and real-time camera control capabilities. In this paper, we describe a portable and single-PC based instructional video capture system, which incorporates a variety of computer vision techniques for its video directing and close-up region specification. We describe the technologies used, including the laser-pointer detections, instructor’s lip tracking and individual teaching object recognition. As the same time, we also explain how we have achieved both low-cost and portability property in our design.


Gaussian Mixture Model Scale Invariant Feature Transform Laser Pointer Static Camera Camera Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shimada, A., Suganuma, A., Taniguchi, R.: Automatic Camera Control System for a Distant Lecture Based on Estimation of Teacher’s Behavior. In: International Conference on Computers and Advanced Technology in Education (2004)Google Scholar
  2. 2.
    Ozeki, M., Nakamura, Y., Ohta, Y.: Automated camerawork for capturing desktop presentations - camerawork design and evaluation in virtual and real scenes. In: 1st European Conference on Visual Media Production (CVMP) (2004)Google Scholar
  3. 3.
    Bianchi, M.: Automatic video production of lectures using an intelligent and aware environment. In: 3rd international conference on Mobile and ubiquitous multimedia, College Park, Maryland (2004)Google Scholar
  4. 4.
    Xu, R.Y.D., Jin, J.S.: Adapting Computer Vision Algorithms to Real-time Peer-to-Peer E-learning: Review, Issues and Solutions. In: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Vancouver, Canada (to appear, 2005)Google Scholar
  5. 5.
    Xu, R.Y.D., Jin, J.S.: Individual Object Interaction for Camera Control and Multimedia Synchronization. In: 31st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006), Toulouse, France (to appear, 2006)Google Scholar
  6. 6.
    Lowe, D.: Distinctive image features from scale invariant key points. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Olsen, D.R., Nielsen, T.: Laser pointer interaction. In: Proceedings of the SIGCHI conference on Human factors in computing systems, Seattle, Washington, United States (2001)Google Scholar
  8. 8.
    Eckert, R.R., Moore, J.A.: The classroom of the 21st century: The interactive learning wall 32, 33–40 (2000)Google Scholar
  9. 9.
    Oh, J., Stuerzlinger, W.: Laser pointers as collaborative pointing devices. Graphics Interface 2002 (2002)Google Scholar
  10. 10.
    Olsen, D.: A design tool for camera-based interaction. In: Proceedings of the SIGCHI conference on Human factors in computing systems (CHI 2003) (2003)Google Scholar
  11. 11.
    Shi, Y., Xie, W., Xu, G., Shi, R., Chen, E., Mao, Y., Liu, F.: The Smart Classroom: Merging Technologies for Seamless Tele-Education. Pervasive Computing 2, 47–55 (2003)CrossRefGoogle Scholar
  12. 12.
    Ahlborn, B.A., Thompson, D.C., Kreylos, O., Hamann, B., Staadt, O.G.: A practical system for laser pointer interaction on large displays. In: ACM Symposium on Virtual Reality Software and Technology (VRST 2005) (2005)Google Scholar
  13. 13.
    Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)MATHGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001 (2001)Google Scholar
  15. 15.
    Cheng, K., Takatsuka, M.: Real-time Monocular Tracking of View Frustum for Large Screen Human-Computer Interaction. In: Twenty-Eighth Australasian Computer Science Conference (ACSC 2005), Newcastle, Australia (2005)Google Scholar
  16. 16.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 25, 564–575 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Richard Y. D. Xu
    • 1
  1. 1.School of Information TechnologyCharles Sturt UniversityBathurstAustralia

Personalised recommendations