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文件名称:Tracking_object_
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Tracking source: Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed
that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and
that modeling such dependency should improve tracking robustne-Tracking source: Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed
that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and
that modeling such dependency should improve tracking robustness
that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and
that modeling such dependency should improve tracking robustne-Tracking source: Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed
that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and
that modeling such dependency should improve tracking robustness
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trackingext.m
drawrect.m
getrect.m
drawrect.m
getrect.m
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