Object tracking using SIFT features and mean shift

Huiyu Zhou*, Yuan Yuan, Chunmei Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation-maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Original languageEnglish
Pages (from-to)345-352
Number of pages8
JournalComputer Vision and Image Understanding
Issue number3
Early online date29 Aug 2008
Publication statusPublished - Mar 2009


  • color histogram
  • expectation-maximization
  • mean shift
  • object tracking
  • SIFT features


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