@inproceedings{Choi:2016:10.1109/CVPR.2016.468,
author = {Choi, J and Chang, H and Jeong, J and Demiris, Y and Choi, JY},
doi = {10.1109/CVPR.2016.468},
publisher = {IEEE},
title = {Visual tracking using attention-modulated disintegration and integration},
url = {http://dx.doi.org/10.1109/CVPR.2016.468},
year = {2016}
}

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TY  - CPAPER
AB  - In this paper, we present a novel attention-modulatedvisual tracking algorithm that decomposes an object intointo multiple cognitive units, and trains multiple elemen-tary trackers in order to modulate the distribution of at-tention according to various feature and kernel types. Inthe integration stage it recombines the units to memorizeand recognize the target object effectively. With respectto the elementary trackers, we present a novel attentionalfeature-based correlation filter (AtCF) that focuses on dis-tinctive attentional features. The effectiveness of the pro-posed algorithm is validated through experimental compar-ison with state-of-the-art methods on widely-used trackingbenchmark datasets.
AU  - Choi,J
AU  - Chang,H
AU  - Jeong,J
AU  - Demiris,Y
AU  - Choi,JY
DO  - 10.1109/CVPR.2016.468
PB  - IEEE
PY  - 2016///
SN  - 1063-6919
TI  - Visual tracking using attention-modulated disintegration and integration
UR  - http://dx.doi.org/10.1109/CVPR.2016.468
UR  - http://ieeexplore.ieee.org/document/7780837/
UR  - http://hdl.handle.net/10044/1/30903
ER  - 

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