Unsupervised moving object detection with on-line generalized hough transform

Authors:
Jie Xu;Yang Wang;Wei Wang;Jun Yang;Zhidong Li
Affiliations:
University of New South Wales, National ICT Australia;University of New South Wales, National ICT Australia;University of New South Wales, National ICT Australia;University of New South Wales, National ICT Australia;University of New South Wales, National ICT Australia
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

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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.