Visual tracking via efficient kernel discriminant subspace learning

  title={Visual tracking via efficient kernel discriminant subspace learning},
  author={Chunhua Shen and Anton van den Hengel and Michael J. Brooks},
  journal={IEEE International Conference on Image Processing 2005},
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning strategy to find a discriminative model which distinguishes the tracked object from the background. Principal component analysis and linear discriminant analysis have been applied to this problem with some success. These techniques are limited, however, by the fact that they are capable only of identifying linear… CONTINUE READING


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