2011 | OriginalPaper | Buchkapitel
Unsupervised Moving Object Detection with On-line Generalized Hough Transform
verfasst von : Jie Xu, Yang Wang, Wei Wang, Jun Yang, Zhidong Li
Erschienen in: Computer Vision – ACCV 2010
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.