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

Jing Wen, Xinbo Gao*, Yuan Yuan, Dacheng Tao, Jie Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)827-839
Number of pages13
Issue number4-6
Early online date20 Nov 2009
Publication statusPublished - Jan 2010

Bibliographical note

Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)


  • biased discriminant analysis
  • incremental tensor biased discriminant analysis
  • tensor biased discriminant analysis
  • visual tracking


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