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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)

Abstract

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.

Keywords

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.

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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

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