Can Diversity Amongst Learners Improve Online Object Tracking?

Georg Nebehay, W. Chibamu, P. R. Lewis, A. Chandra, R. Pflugfelder and X. Yao

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 learn- ing, 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.

Published In

International Workshop on Multiple Classifier Systems, 2013.
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BibTex

@inproceedings{Nebehay2013MCS,
    author = {Nebehay, Georg and Chibamu, Walter and Lewis, Peter R. and Chandra, Arjun and Pflugfelder, Roman and Yao, Xin},
    booktitle = {Multiple Classifier Systems},
    doi = {10.1007/978-3-642-38067-9\_19},
    pages = {212--223},
    publisher = {Springer Berlin Heidelberg},
    title = {Can Diversity amongst Learners Improve Online Object Tracking?},
    url = {http://dx.doi.org/10.1007/978-3-642-38067-9\_19},
    year = {2013}
}

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