Pedestrian Recognition in Far-Infrared Images by Combining Boosting-Based Detection and Skeleton-Based Stochastic Tracking

  • Ryusuke Miyamoto
  • Hiroki Sugano
  • Hiroaki Saito
  • Hiroshi Tsutsui
  • Hiroyuki Ochi
  • Ken’ichi Hatanaka
  • Yukihiro Nakamura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Nowadays, pedestrian recognition in far-infrared images toward realizing a night vision system becomes a hot topic. However, sufficient performance could not be achieved by conventional schemes for pedestrian recognition in far-infrared images. Since the properties of far-infrared images are different from visible images, it is not known what kind of scheme is suitable for pedestrian recognition in far-infrared images. In this paper, a novel pedestrian recognition scheme combining boosting-based detection and skeleton-based stochastic tracking suitable for recognition in far-infrared images is proposed. Experimental results by using far-infrared sequences show the proposed scheme achieves highly accurate pedestrian recognition by combining accurate detection with few false positives and accurate tracking.


Pedestrian Detection Accurate Tracking Skeleton Model Mersenne Twister Pedestrian Tracking 
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.
    Xu, F., Fujimura, K.: Pedestrian detection and tracking with night vision. IEEE Trans. on ITS 6, 63–71 (2005)Google Scholar
  2. 2.
    Tan, T., Hattori, H., Watanabe, M., Nagaoka, N.: Development of night-vision system. IEEE Trans. on ITS 3, 203–209 (2002)Google Scholar
  3. 3.
    Fang, Y., Yamada, K., Ninomiya, Y., Horn, B.K.P., Masaki, I.: A shape-independent method for pedestrian detection with far-infrared images. IEEE Trans. on VT 53, 1679–1697 (2004)Google Scholar
  4. 4.
    Liu, X., Fujimura, K.: Pedestrian detection using stereo night vision. IEEE Trans. on VT 53, 1657–1665 (2004)Google Scholar
  5. 5.
    Yasuno, M., Yasuda, N., Aoki, M.: Pedestrian detection and tracking in far infrared images. In: Proc. of CVPRW, pp. 125–125 (2004)Google Scholar
  6. 6.
    Sun, H., Hua, C., Luo, Y.: A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car. In: Proc. of ICIG, pp. 120–123 (2004)Google Scholar
  7. 7.
    Bertozzi, M., Broggi, A., Fascioli, A., Graf, T., Meinecke, M.M.: Pedestrian detection for driver assistance using multiresolution infrared vision. IEEE Trans. on VT 53, 1666–1678 (2004)Google Scholar
  8. 8.
    Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Proggio, T.: Pedestrian detection using wavelet templates. In: Proc. of CVPR, pp. 193–199 (1997)Google Scholar
  9. 9.
    Papageorgiou, C., Poggio, T.: Trainable pedestrian detection. In: Proc. of ICIP, vol. 4, pp. 35–39 (1999)Google Scholar
  10. 10.
    Soga, M., Kato, T., Ohta, M., Ninomiya, Y.: Pedestrian detection using stereo vision and tracking. In: Proc. of The 11th World Congress on Intelligent Transport Systems (2004)Google Scholar
  11. 11.
    Zhao, L., Thorpe, C.E.: Stereo- and neural network-based pedestrian detection. IEEE Trans. on ITS 01, 148–154 (2000)Google Scholar
  12. 12.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63, 153–161 (2005)CrossRefGoogle Scholar
  13. 13.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. on SMC 34, 334–352 (2004)Google Scholar
  14. 14.
    Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A., Chapuis, R., Chausse, F.: Pedestrian localization and tracking system with kalman filtering. In: Proc. of IEEE Intelligent Vehicles Symposium, pp. 584–589 (2004)Google Scholar
  15. 15.
    Isard, M., Blake, A.: Condensation|conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  16. 16.
    Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3D human figures using 2D image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 1–18. Springer, Heidelberg (2000)Google Scholar
  17. 17.
    Sigal, L., Bhatia, S., Roth, S., Black, M.J., Isard, M.: Tracking loose-limbed people. In: Proc. of CVPR, vol. 1, pp. 421–428 (2004)Google Scholar
  18. 18.
    Balan, A.O., Sigal, L., Black, M.J.: A quantitative evaluation of video-based 3D person tracking. In: Proc. of IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 349–356 (2005)Google Scholar
  19. 19.
    Kitagawa, G.: Monte-carlo filter and smoother for nongaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5(1), 1–25 (1996)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Ashida, J., Miyamoto, R., Tsutsui, H., Onoye, T., Nakamura, Y.: Probabilistic pedestrian tracking based on a skeleton model. In: Proc. of ICIP (2006) (to appear)Google Scholar
  21. 21.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Miyamoto, R., Ashida, J., Tsutsui, H., Nakamura, Y.: Skeleton based stochastic pedestrian tracking for surveillance. In: Proc. of The 10th WMSCI, July 2006, vol. V, pp. 206–211 (2006)Google Scholar
  23. 23.
    Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans. on Modeling and Computer Simulation 8, 3–30 (1998)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryusuke Miyamoto
    • 1
    • 2
  • Hiroki Sugano
    • 1
    • 2
  • Hiroaki Saito
    • 3
  • Hiroshi Tsutsui
    • 1
  • Hiroyuki Ochi
    • 1
    • 2
  • Ken’ichi Hatanaka
    • 3
  • Yukihiro Nakamura
    • 1
    • 2
  1. 1.Dept. of Communications and Computer EngineeringKyoto UniversityKyotoJapan
  2. 2.Kyoto Center, Synthesis CorporationKyotoJapan
  3. 3.Sumitomo Electric Industries, Ltd.OsakaJapan

Personalised recommendations