A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE

Eugenio Aguirre, Miguel García-Silvente, Rui Paúl, Rafael Muñoz-Salinas

2007

Abstract

Despite of the advances achieved in the past years in order to design more natural interfaces between intelligent systems and humans, there is still a great effort to be done. Considering a robot as an intelligent system, determining the interest of the surrounding people in interacting with it is an interesting ability to achieve. That information can be used to establish a more natural communication with humans as well as to design more sophisticated policies for resource assignment. This paper proposes a fuzzy system that establishes a level of possibility about the degree of interest that people around the robot have in interacting with it. First, a method to detect and track persons using stereo vision is briefly explained. Once the visible people is spotted, their interest in interacting with the robot is computed by analyzing its position and its level of attention towards the robot. These pieces of information are combined using fuzzy logic. The level of attention of a person is calculated by analyzing the pose of his head that is estimated in real-time by a view based approach using Support Vector Machines (SVM). Although the proposed system is based only on visual information, its modularity and the use of fuzzy logic make it easier to incorporate in the future other sources of information to estimate with higher precision the interest of people. At the end of the paper, some experiments are shown that validate the proposal and future work is addressed.

References

  1. Aguirre, E. and González, A. (2000). Fuzzy behaviors for mobile robot navigation: Design, coordination and fusion. International Journal of Approximate Reasoning, 25:255-289.
  2. Bennewitz, M., Faber, F., Joho, D., Schreiber, M., and Behnke, S. (2005). Integrating vision and speech for conversations with multiple persons. In IROS'05: Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 2523 - 2528.
  3. Bien, Z. and Song, W. (2003). Blend of soft computing techniques for effective human-machine interaction in service robotic systems. Fuzzy Sets and Systems, 134(1):5-25.
  4. Birchfield, S. (1998). Elliptical Head Tracking Using Intensity Gradients and Color Histograms. In IEEE Conference on Computer Vision and Pattern Recognition, pages 232-237.
  5. Chang, C. and Lin, C. (2006). a library for support vector http://www.csie.ntu.edu.tw/ cjlin/libsvm/.
  6. Comaniciu, D., Ramesh, V., and Meer, P. (2000). Real-Time Tracking of Non-Rigid Objects using Mean Shift. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 142-149.
  7. Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction To Support Vector Machines (and other Kernel Based Methods). Cambridge University Press.
  8. Darrell, T., Demirdjian, D., Checka, N., and Felzenszwalb, P. (2001). Plan-view trajectory estimation with dense stereo background models. In Eighth IEEE International Conference on Computer Vision (ICCV 2001), volume 2, pages 628 - 635.
  9. Fritsch, J., Kleinehagenbrock, M., Lang, S., Plötz, T., Fink, G. A., and Sagerer, G. (2003). Multi-modal anchoring for human-robot interaction. Robotics and Autonomous Systems, 43(2-3):133-147.
  10. Ghidary, S. S., Nakata, Y., Saito, H., Hattori, M., and Takamori, T. (2002). Multi-modal interaction of human and home robot in the context of room map generation. Autonomous Robots, 13(2):169-184.
  11. Haritaoglu, I., Beymer, D., and Flickner, M. (2002). Ghost 3d: detecting body posture and parts using stereo. In Workshop on Motion and Video Computing, pages 175 - 180.
  12. Harville, M. (2004). Stereo person tracking with adaptive plan-view templates of height and occupancy statistics. Image and Vision Computing, 2:127-142.
  13. Hayashi, K., Hashimoto, M., Sumi, K., and Sasakawa, K. (2004). Multiple-person tracker with a fixed slanting stereo camera. In 6th IEEE International Conference on Automatic Face and Gesture Recognition, pages 681-686.
  14. Henry, G. and Dunteman (1989). Principal Components Analysis. SAGE Publications.
  15. Intel (2005). OpenCV: Open source Computer Vision library.
  16. Kuhn, H. W. (1955). The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83-97.
  17. Kulic, D. and Croft, E. (2003). Estimating intent for human robot interaction. In International Conference on Advanced Robotics, pages 810-815.
  18. Lienhart, R. and Maydt, J. (2002). An Extended Set of Haar-Like Features for rapid Object detection. In IEEE Conf. on Image Processing, pages 900-903.
  19. Mun˜oz-Salinas, R., Aguirre, E., and Garc ía-Silvente, M. (2006). People detection and tracking using stereo vision and color. To appear in Image and Vision Computing. Available online at www.sciencedirect.com.
  20. PtGrey (2005). Bumblebee. Binocular stereo vision camera system. http://www.ptgrey.com/products/bumblebee/index.html.
  21. Saffiotti, A. (1997). The uses of fuzzy logic in autonomous robot navigation. Soft Computing, 1:180-197.
  22. Snidaro, L., Micheloni, C., and Chiavedale, C. (2005). Video security for ambient intelligence. IEEE Transactions on Systems, Man and Cybernetics, Part A, 35:133 - 144.
  23. Song, W., Kim, D., Kim, J., and Bien, Z. (2001). Visual servoing for a user's mouth with effective intention reading in a wheelchair-based robotic arm. In ICRA, pages 3662-3667.
  24. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Conf. on Computer Vision and Pattern Recognition, pages 511-518.
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Paper Citation


in Harvard Style

Aguirre E., García-Silvente M., Paúl R. and Muñoz-Salinas R. (2007). A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-83-2, pages 181-188. DOI: 10.5220/0001632601810188


in Bibtex Style

@conference{icinco07,
author={Eugenio Aguirre and Miguel García-Silvente and Rui Paúl and Rafael Muñoz-Salinas},
title={A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2007},
pages={181-188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001632601810188},
isbn={978-972-8865-83-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE
SN - 978-972-8865-83-2
AU - Aguirre E.
AU - García-Silvente M.
AU - Paúl R.
AU - Muñoz-Salinas R.
PY - 2007
SP - 181
EP - 188
DO - 10.5220/0001632601810188