Object tracking using SIFT features and mean shift

Huiyu Zhou, Yuan Yuan, Chunmei Shi

科研成果: 期刊稿件文章同行评审

570 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)345-352
页数8
期刊Computer Vision and Image Understanding
113
3
DOI
出版状态已出版 - 3月 2009
已对外发布

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