Online learning of linear predictors for real-time tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Efficient learning of linear predictors using dimensionality reduction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Efficient algorithms that track targets with a constant aspect (rigid objects, for example) are often based on appearance models. The simplest models linearly predict motion parameters from gray-scale variations measured at features. Choosing the features and training the predictor is done during a preliminary off-line stage.This paper presents several methods that improve the features selection process by filtering out some features from a given set. In particular, we are interested in the SVD-based subset selection procedure proposed by Golub and Van Loan.We show a significant improvement of tracking performance when our method filters Moravec, Harris, KLT or SUSAN features. We conclude that individually good selected features may not build a good subset and that a good spatial distribution of the features is paramount.