• DocumentCode
    119751
  • Title

    Non parametric tool for vision detection analysis

  • Author

    Azzam, Riad ; Aouf, Nabil

  • Author_Institution
    Centre for Electron. Warfare, Cranfield Univ., Shrivenham, UK
  • fYear
    2014
  • fDate
    10-12 Sept. 2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In this work, we deal with the problem of moving object detection using a non-parametric tool represented by the Gaussian process for classification. The technique used relies on the background subtraction approach for motion detection. In this context, a segmentation step is first implemented for pixel clustering before a binary Gaussian process classifier is applied to determine which pixel cluster those of news images belong to. The unclassified pixels are, therefore, labelled as detected targets. This proposed approach enables motion detection to be completed in a comparatively a short execution time with acceptable results. The results outlined here show the effectiveness of the approach to known background subtraction methods.
  • Keywords
    Gaussian processes; image classification; image motion analysis; image resolution; image segmentation; nonparametric statistics; object detection; pattern clustering; Gaussian process; background subtraction approach; binary Gaussian process classifier; motion detection; moving object detection problem; nonparametric tool; pixel clustering; target detection; vision detection analysis; Approximation methods; Clustering algorithms; Computational modeling; Computer vision; Gaussian processes; Image segmentation; Prototypes; Background Segmentation; Binary Gaussian Classifier; Gaussian Process; Vision detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ELMAR (ELMAR), 2014 56th International Symposium
  • Conference_Location
    Zadar
  • Type

    conf

  • DOI
    10.1109/ELMAR.2014.6923305
  • Filename
    6923305