Pedestrian Detection Using Stereo and Biometric Information

  • Philip Kelly
  • Eddie Cooke
  • Noel O’Connor
  • Alan Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


A method for pedestrian detection from real world outdoor scenes is presented in this paper. The technique uses disparity information, ground plane estimation and biometric information based on the golden ratio. It can detect pedestrians even in the presence of severe occlusion or a lack of reliable disparity data. It also makes reliable choices in ambiguous areas since the pedestrian regions are initiated using the disparity of head regions. These are usually highly textured and unoccluded, and therefore more reliable in a disparity image than homogeneous or occluded regions.


Golden Ratio Pedestrian Detection Disparity Estimation Biometric Information Disparity Information 
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.


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  1. 1.
    Uchida, K., Miura, J., Shirai, Y.: Tracking multiple pedestrians in crowd. In: Workshop of Machine Vision and its Applications, pp. 533–536 (2000)Google Scholar
  2. 2.
    Pai, C., Tyan, H., Liang, Y., Liao, H., Chen, S.: Pedestrian detection and tracking at crossroads. In: PR, vol. 37, pp. 1025–1034 (2004)Google Scholar
  3. 3.
    Gavrila, D., Giebel, J.: Shape-based pedestrian detection and tracking. In: IEEE Intelligent Vehicles Symposium, pp. 215–220 (2002)Google Scholar
  4. 4.
    Terada, K., Yoshida, D., Oe, S., Yamaguchi, J.: A counting method of the number of passing people using a stereo camera. In: IEEE Conference on Industrial Electronics, vol. 3, pp. 1318–1323 (1999)Google Scholar
  5. 5.
    Darrell, T., Gordon, G., Woodfill, M.H.: Integrated person tracking using stereo, color and pattern detection. International Journal of Computer Vision 37, 175–185 (2000)zbMATHCrossRefGoogle Scholar
  6. 6.
    Zhao, L., Thorpe, C.: Stereo and neural network-based pedestrian detection. IEEE Transactions on Intelligent Transportation Systems 1, 148–154 (2000)CrossRefGoogle Scholar
  7. 7.
    Kelly, P., Cooke, E., O’Connor, N., Smeaton, A.: 3d image analysis for pedestrian detection. In: Image Analysis for Multimedia Interactive Services (WIAMIS), pp. 177–180 (2006)Google Scholar
  8. 8.
    Kelly, P., Beardsley, P., Cooke, E., O’Connor, N., Smeaton, A.: Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies. In: The IEE International Conference on Visual Information Engineering, pp. 393–400 (2005)Google Scholar
  9. 9.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision, 2nd edn. PWS Publishing (1999)Google Scholar
  10. 10.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Philip Kelly
    • 1
  • Eddie Cooke
    • 1
  • Noel O’Connor
    • 1
  • Alan Smeaton
    • 1
  1. 1.Centre for Digital Video Processing, Adaptive Information ClusterDublin City UniversityIreland

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