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Abstract

This paper presents a method for evaluating multiple feature spaces while tracking, and for 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. We develop an on-line feature ranking mechanism based on the two-class variance ratio measure, applied to log likelihood values computed from empirical distributions of object and background pixels with respect to a given feature. This feature ranking mechanism is embedded in a tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented to illustrate how the method adapts to changing appearances of both tracked object and scene background.

Keywords

tracking, feature selection

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Robert Collins and Yanxi Liu, "On-Line Selection of Discriminative Tracking Features," tech. report CMU-RI-TR-03-12, Robotics Institute, Carnegie Mellon University, April, 2003

BibTeX Reference

@techreport{Collins_2003_4368,
   author = "Robert Collins and Yanxi Liu",
   title = "On-Line Selection of Discriminative Tracking Features",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "2003",
   number= "CMU-RI-TR-03-12",
   address= "Pittsburgh, PA",
}

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