Support vector tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence(2004)

引用 1715|浏览556
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摘要
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.
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关键词
successive frame,SVM classification score,Support Vector Machine,Support Vector Tracking,classification stage,coarse-to-fine approach,image sequence,intensity difference function,large motion,optic-flow-based tracker,support vector tracking
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