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Carnegie Mellon Robotics Institute

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Abstract

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 online feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. This feature selection 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.

Notes

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

Number of pages: 7

Text Reference

Robert Collins and Yanxi Liu, "On-Line Selection of Discriminative Tracking Features," Proceedings of the 2003 International Conference of Computer Vision (ICCV '03), October, 2003, pp. 346 - 352.

BibTeX Reference

@inproceedings{Collins_2003_4435,
   author = "Robert Collins and Yanxi Liu",
   title = "On-Line Selection of Discriminative Tracking Features",
   booktitle = "Proceedings of the 2003 International Conference of Computer Vision (ICCV '03)",
   pages = "346 - 352",
   month = "October",
   year = "2003",
   volume = "1",
}

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