Journal
COMPUTER VISION AND IMAGE UNDERSTANDING
Volume 113, Issue 3, Pages 345-352Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2008.08.006
Keywords
Object tracking; Color histogram; Mean shift; SIFT features; Expectation-maximization
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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. (C) 2008 Elsevier Inc. All Fights reserved.
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