• DocumentCode
    120808
  • Title

    Object tracking using a new statistical multivariate hotelling´s T2 approach

  • Author

    Acharya, Anuja Kumar ; Sahoo, Bibhudatta ; Swain, Biswa Ranjan

  • Author_Institution
    Sch. of Comput. Eng., KIIT Univ., Bhubaneswar, India
  • fYear
    2014
  • fDate
    21-22 Feb. 2014
  • Firstpage
    969
  • Lastpage
    972
  • Abstract
    In this paper we proposed a new statistical multivariate method for tracking an object in a video. This method is based on the Hottelling T2 test which is designed to provide a global significance test for the difference between two region or two group with simultaneously measured multiple dependent or independent variables. An object to be tracked can be found by comparing its multivariate mean in the successive frame of the video. The T2 value give the measurement of the difference of two mean vector. In this approach the object window containing the matrix of intensity value is transformed into a set of feature vector. These set of features is compared using multivariate T2 test in the successive frame for the significant matching of the object in its nearest locality. It is observed that higher the T2 value more is the chance of mismatching and lower the T2 value more is the chance of matching the multi attribute. Simulation result shows that the proposed method is capable of accurately detecting the non rigid, moving object in stationary as well as non stationary camera with noisy and occlusion environment.
  • Keywords
    cameras; image matching; image sequences; object tracking; statistical testing; video signal processing; Hottelling T2 test; T2 value; feature vector; global significance test; intensity value matrix; mean vector difference; multiattribute matching; nonstationary camera; object matching; object tracking; occlusion environment; statistical multivariate Hotelling T2 approach; statistical multivariate method; video frame; Cameras; Conferences; Feature extraction; Object tracking; Target tracking; Vectors; Hottelling; Multivariate; Statistic; feature vector;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advance Computing Conference (IACC), 2014 IEEE International
  • Conference_Location
    Gurgaon
  • Print_ISBN
    978-1-4799-2571-1
  • Type

    conf

  • DOI
    10.1109/IAdCC.2014.6779454
  • Filename
    6779454