Can Diversity amongst Learners Improve Online Object Tracking?
نویسندگان
چکیده
We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classi er that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classi cation and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved.
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