Abstract
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 classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification 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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Nebehay, G., Chibamu, W., Lewis, P.R., Chandra, A., Pflugfelder, R., Yao, X. (2013). Can Diversity amongst Learners Improve Online Object Tracking?. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_19
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DOI: https://doi.org/10.1007/978-3-642-38067-9_19
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