Unsupervised Moving Object Detection with On-line Generalized Hough Transform

  • Jie Xu
  • Yang Wang
  • Wei Wang
  • Jun Yang
  • Zhidong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Generalized Hough Transform-based methods have been successfully applied to object detection. Such methods have the following disadvantages: (i) manual labeling of training data ; (ii) the off-line construction of codebook. To overcome these limitations, we propose an unsupervised moving object detection algorithm with on-line Generalized Hough Transform. Our contributions are two-fold: (i) an unsupervised training data selection algorithm based on Multiple Instance Learning (MIL); (ii) an on-line Extremely Randomized Trees construction algorithm for on-line codebook adaptation. We evaluate the proposed algorithm on three video datasets. The experimental results show that the proposed algorithm achieves comparable performance to the supervised detection method with manual labeling. They also show that the proposed algorithm outperforms the previously proposed unsupervised learning algorithm.


Leaf Node Object Detection Precision Recall Curve Multiple Instance Learn Manual Label 
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.
    Dalai, N., Triggs, B., Rhone-Alps, I., Montbonnot, F.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1 (2005)Google Scholar
  2. 2.
    Munder, S., Gavrila, D.: An experimental study on pedestrian classification. TPAMI (2006)Google Scholar
  3. 3.
    Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)Google Scholar
  4. 4.
    Fergus, R., Perona, P., Zisserman, A.: Weakly supervised scale-invariant learning of models for visual recognition. IJCV (2007)Google Scholar
  5. 5.
    Leibe, B., Leonardis, A., Schiele, B.: Robust Object Detection with Interleaved Categorization and Segmentation. IJCV (2008)Google Scholar
  6. 6.
    Okada, R.: Discriminative Generalized Hough Transform for Object Detection. In: ICCV (2009)Google Scholar
  7. 7.
    Balcan, M., Blum, A., Yang, K.: Co-training and expansion: Towards bridging theory and practice. In: NIPS (2005)Google Scholar
  8. 8.
    Javed, O., Ali, S., Shah, M.: Online detection and classification of moving objects using progressively improving detectors. In: CVPR (2005)Google Scholar
  9. 9.
    Wu, B., Nevatia, R.: Improving part based object detection by unsupervised, online boosting. In: CVPR (2007)Google Scholar
  10. 10.
    Nair, V., Clark, J.: An unsupervised, online learning framework for moving object detection. In: CVPR (2004)Google Scholar
  11. 11.
    Roth, P., Grabner, H., Skocaj, D., Bischof, H., Leonardis, A.: On-line conservative learning for person detection. In: VS-PETS (2005)Google Scholar
  12. 12.
    Gall, J., Lempitsky, V.: Class-Specific Hough Forests for Object Detection. In: CVPR (2009)Google Scholar
  13. 13.
    Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line Random Forests. In: The 3rd On-line Learning for Computer Vision Workshop (2009)Google Scholar
  14. 14.
    Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: NIPS (1998)Google Scholar
  15. 15.
    Li, W., Yeung, D.: Localized content-based image retrieval through evidence region identification. In: CVPR (2009)Google Scholar
  16. 16.
    Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: ICCV (2009)Google Scholar
  17. 17.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning (2006)Google Scholar
  18. 18.
    Canny, J.: A computational approach to edge detection. TPAMI (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jie Xu
    • 1
  • Yang Wang
    • 1
  • Wei Wang
    • 1
  • Jun Yang
    • 1
  • Zhidong Li
    • 1
  1. 1.National ICT AustraliaUniversity of New South WalesAustralia

Personalised recommendations