• DocumentCode
    3562394
  • Title

    Pedestrian detection using covariance features

  • Author

    Said, Yahia ; Salah, Yahia ; Atri, Mohamed

  • Author_Institution
    Lab. of Electron. & Microelectron. (EμE), Univ. of Monastir, Monastir, Tunisia
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Detecting pedestrians is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. The Region Covariance Matrix (RCM) descriptors show experimentally significantly out-performs existing feature sets for pedestrian detection. In this paper, we present an efficient features extraction scheme: the Integral CovReg, inspired from Region Covariance Matrix (RCM) descriptors, combined with SVM classifier for pedestrian detection.
  • Keywords
    covariance matrices; feature extraction; image classification; object detection; support vector machines; Integral CovReg; SVM classifier; covariance features; feature extraction; image background; image motion; pedestrian detection; region covariance matrix descriptors; Conferences; Covariance matrices; Detectors; Feature extraction; Histograms; Support vector machines; Vectors; Image descriptor; Integral CovReg; Pedestrian detection; SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, Applications and Systems Conference (IPAS), 2014 First International
  • Print_ISBN
    978-1-4799-7068-1
  • Type

    conf

  • DOI
    10.1109/IPAS.2014.7043307
  • Filename
    7043307
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3562394