Measurement error estimation for feature tracking

Kevin Nickels, Seth Hutchinson

Research output: Contribution to journalConference articlepeer-review


Performance estimation for feature tracking is a critical issue, if feature tracking results are to be used intelligently. In this paper, we derive quantitative measures for the spatial accuracy of a particular feature tracker. This method uses the results from the sum-of-squared-differences correlation measure commonly used for feature tracking to estimate the accuracy (in the image plane) of the feature tracking result. In this way, feature tracking results can be analyzed and exploited to a greater extent without placing undue confidence in inaccurate results or throwing out accurate results. We argue that this interpretation of results is more flexible and useful than simply using a confidence measure on tracking results to accept or reject features. For example, an extended Kalman filtering framework can assimilate these tracking results directly to monitor the uncertainty in the estimation process for the state of an articulated object.

Original languageEnglish (US)
Pages (from-to)3230-3235
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - 1999
EventProceedings of the 1999 IEEE International Conference on Robotics and Automation, ICRA99 - Detroit, MI, USA
Duration: May 10 1999May 15 1999

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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