Online selection of discriminative tracking features

This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation…

• UT
  Universitatea Tehnică din Cluj-Napoca
• NI
  National Institutes of Health
• DA
  Defense Advanced Research Projects Agency
• UO
  University of Massachusetts Amherst