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Object tracking using SIFT features and mean shift
- Source :
- Computer Vision & Image Understanding; Mar2009, Vol. 113 Issue 3, p345-352, 8p
- Publication Year :
- 2009
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 10773142
- Volume :
- 113
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Computer Vision & Image Understanding
- Publication Type :
- Academic Journal
- Accession number :
- 36434392
- Full Text :
- https://doi.org/10.1016/j.cviu.2008.08.006