Improving feature tracking with robust statistics

A. Fusiello, E. Trucco, T. Tommasini, V. Roberto

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)


This paper addresses robust feature tracking. The aim is to track point features in a sequence of images and to identify unreliable features resulting from occlusions, perspective distortions and strong intensity changes. We extend the well-known Shi-Tomasi-Kanade tracker by introducing an automatic scheme for rejecting spurious features. We employ a simple and efficient outliers rejection rule, called X84, and prove that its theoretical assumptions are satisfied in the feature tracking scenario. Experiments with real and synthetic images confirm that our algorithm consistently discards unreliable features; we show a quantitative example of the benefits introduced by the algorithm for the case of fundamental matrix estimation. The complete code of the robust tracker is available via ftp. © 1999 Springer-Verlag London Limited.

Original languageEnglish
Pages (from-to)312-320
Number of pages9
JournalPattern Analysis and Applications
Issue number4
Publication statusPublished - 1999


  • Feature tracking
  • Motion analysis
  • Optical flow
  • Registration
  • Robust statistics
  • X84


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