Incremental tensor biased discriminant analysis: A new color-based visual tracking method

  • Authors:
  • Jing Wen;Xinbo Gao;Yuan Yuan;Dacheng Tao;Jie Li

  • Affiliations:
  • School of Electronic Engineering, Xidian University, No. 2, South Taibai Road, Xi'an 710071, China;School of Electronic Engineering, Xidian University, No. 2, South Taibai Road, Xi'an 710071, China;School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom;School of Computer Engineering, Nanyang Technological University, Singapore;School of Electronic Engineering, Xidian University, No. 2, South Taibai Road, Xi'an 710071, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2010

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Abstract

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion.