Research output per year
Research output per year
Jing Wen, Xinbo Gao*, Yuan Yuan, Dacheng Tao, Jie Li
Research output: Contribution to journal › Article › peer-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 language | English |
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Pages (from-to) | 827-839 |
Number of pages | 13 |
Journal | Neurocomputing |
Volume | 73 |
Issue number | 4-6 |
Early online date | 20 Nov 2009 |
DOIs | |
Publication status | Published - Jan 2010 |
Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review