International Journal of Computer Vision

, Volume 71, Issue 2, pp 143–160 | Cite as

Pedestrian Detection via Periodic Motion Analysis

  • Yang RanEmail author
  • Isaac Weiss
  • Qinfen Zheng
  • Larry S. Davis


We describe algorithms for detecting pedestrians in videos acquired by infrared (and color) sensors. Two approaches are proposed based on gait. The first employs computationally efficient periodicity measurements. Unlike other methods, it estimates a periodic motion frequency using two cascading hypothesis testing steps to filter out non-cyclic pixels so that it works well for both radial and lateral walking directions. The extraction of the period is efficient and robust with respect to sensor noise and cluttered background. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence by Maximal Principal Gait Angle (MPGA) fitting in the second method. It does not require alignment and continuously estimates the period using a Phase-locked Loop. Both methods are evaluated by experimental results that measure performance as a function of size, movement direction, frame rate and sequence length.


pedestrian detection periodic motion frequency estimation cyclic gait pattern phase-locked loop 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adelson, E.H. and Bergen, J.R. 1985. Spatiotemporal Energy Models for the Perception of Motion. Journal Optical Society of America A, 2(2).Google Scholar
  2. Aggarwal, J.K. and Cai, Q. 1999. Human Motion Analysis: A review. Computer Vision and Image Understanding, 73(3):428–440.CrossRefGoogle Scholar
  3. Allmen, M.C. 1991. Image Sequence Description using Spatiotemporal Flow Curves: Toward Motion-Based Recognition. Ph.D. Dissertation, Computer Sciences Department Technical Report 1040, University of Wisconsin-Madison.Google Scholar
  4. Blanchard, A. 1976. Phase-Locked Loops. New York, NY: John Wiley and Sons.Google Scholar
  5. Boyd, J.E. 2004. Synchronization of Oscillations for Machine Perception of Gaits. Computer Vision and Image Understanding, 96(1):35–59.CrossRefGoogle Scholar
  6. Broggi, A., Bertozzi, M., Fascioli, A., and Sechi, M. 2000.Shape-based Pedestrian Detection. In Proc. IEEE Intell. Veh. Symp., pp. 215–220.Google Scholar
  7. Collins, R.T., Lipton, A.J., and Kanade, T. 2000. Introduction to the Special Section on Video Surveillance. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8):745–746.CrossRefGoogle Scholar
  8. Cutler, R. and Davis, L.S. 2000. Robust Real-time Periodic Motion Detection, Analysis, and Applications. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8):781–796.CrossRefGoogle Scholar
  9. Curio, C., Edelbrunner, J., Kalinke, T., Tzomakas, C., and von Seelen, W. 2000. Walking Pedestrian Recognition. In IEEE Transactions on Intelligent Transportation Systems, 1(3):155–163.CrossRefGoogle Scholar
  10. Efros, Berg, A.C., Mori, G., Malik, J. 2003. Recognizing Action at A Distance. In Proceedings of IEEE International Conference on Computer Vision, pp. 726–733.Google Scholar
  11. Fang, Y., Yamada, K., Ninomiya, Y., Horn, B., and Masaki, I. 2003. Comparison between Infrared-image-based and Visible-image-based Approaches for Pedestrian Detection. IEEE. Intelligent Vehicles Symposium, pp. 505–510.Google Scholar
  12. Fukunaga, K. 1990. Introduction to Statistical Pattern Recognition, 2nd ed. Boston: Academic Press.Google Scholar
  13. Gavrila, D.M. 1999. The Visual Analysis of Human Movement: A survey. Computer Vision and Image understanding, 73(1):82–98.zbMATHCrossRefGoogle Scholar
  14. Hogg. D. 1983. Model-based vision: A Program to See a Walking Person. Image and Vision computing, 1(1):5–20.CrossRefGoogle Scholar
  15. Wang, L., Hu, W., and Tan, T. 2003. Recent Developments in Human Motion Analysis. Pattern Recognition, 36(3):585–601.CrossRefGoogle Scholar
  16. Liu, F. and Picard, R.W. 1998. Finding Periodicity in Space and Time. In Proceedings of the 6th International Conference on Computer Vision, pp. 376–382.Google Scholar
  17. Lindsey, W.C. and Chie, C.M. (eds.) 1986. Phase-Locked Loops. IEEE PRESS Selected Reprint Series, New York, NY: IEEE Press.Google Scholar
  18. Lipton, J., Fujioshi, H., and Patil, R.S. 1998. Moving Target Classification and Tracking from Real-Time Video. In Workshop on Applications of Computer Vision, Princeton, NJ, pp. 8–14.Google Scholar
  19. Maybank, S. and Tan, T. 2000. Introduction to Special Section on Visual Surveillance. International Journal of Computer Vision, 37(2):173–173.CrossRefGoogle Scholar
  20. Nanda, H. and Davis, L. 2002. Probabilistic Template Based Pedestrian Detection in Infrared Videos. In IEEE Intelligent Vehicle Symposium, Versailles, France.Google Scholar
  21. Niyogi, S.A. and Adelson, E.H. 1994. Analyzing and Recognizing Walking Figures in XYT. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 469–474.Google Scholar
  22. Oren, M., Papageorgiou, C.P., Sinha, Osuna, E., and Poggio, T. 2003. Pedestrian Detection Using Wavelet Templates. In IEEE Conference on Computer Vision and Pattern Recognition, 193–199.Google Scholar
  23. Pai, C.-J., Tyan, H-R., Liang, Y.-M., Liao, H.-Y. M., Chen, S.-W. 2004. Pedestrian Detection and Tracking at Crossroads. Pattern Recognition, 37(5):1025–1034.zbMATHCrossRefGoogle Scholar
  24. Papageorgiou, C., Evgeniou, T., and Poggio, T. 1998. A Trainable Pedestrian Detection System. In IEEE Int. Conf. on Intelligent Vehicles, pp. 241–246.Google Scholar
  25. Phillips, P.J., Sarkar, S., Robledo, I., Grother, P., and Bowyer, K.W. 2002. The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm. International Conference on Pattern Recognition, pp. 385–388.Google Scholar
  26. Polana, R. and Nelson, C. 1997. Detection and Recognition of Periodic, Nonrigid Motion. International Journal of Computer Vision, 23(3):261–282CrossRefGoogle Scholar
  27. Quinn, B.G. and Hannan, E.J. 2001. The Estimation and Tracking of Frequency. Cambridge University Press, ISBN 0-521-80446- 9.Google Scholar
  28. Rohr, K. 1994. Towards Model-Based Recognition of Human Movement in Image Sequences. CVGIP: Image Understanding, 59(1):94–115.CrossRefGoogle Scholar
  29. Seitz, S.M. and Dyer, C.R. 1997. View-Invariant Analysis of Cyclic Motion. Int. J. Computer Vision, 25(3):231–251.CrossRefGoogle Scholar
  30. Tsai, P., Shah, M., Keiter, K., and Kasparis, K. 1994. Cyclic Motion Detection. Pattern Recognition, 27(12).Google Scholar
  31. Viola, P., Jones, M., Snow, D. 2003. Detecting Pedestrians using patterns of motion and appearance. In Ninth IEEE International Conference on Computer Vision, pp. 734–782.Google Scholar
  32. Little, J.J. and Boyd, J.E. 1998. Recognizing People by Their Gait: The Shape of Motion. Videre: Journal of Computer Vision Research, The MIT Press, 1(2):24–42.Google Scholar
  33. Zhao, L. and Thorpe, C. 2000. Stereo and Neural Network-based Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, 1(3):148–154.CrossRefGoogle Scholar
  34. Zheng, Q. and Chellappa, R. 1991. Automatic Registration of Oblique Aerial Images. In IEEE International Conference on Image Processing, pp. 218–222.Google Scholar
  35. Zhou, S., Chellappa, R., and Moghaddam, B. 2004.Visual Tracking and Recognition Using Appearance-adaptive Models in Particle Filters. IEEE Transactions on Image Processing, 11:1434–1456.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Yang Ran
    • 1
    Email author
  • Isaac Weiss
    • 1
  • Qinfen Zheng
    • 1
  • Larry S. Davis
    • 1
  1. 1.Center for Automation ResearchUniversity of Maryland at College ParkCollege Park

Personalised recommendations