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Visual Tracking Using Attention-Modulated Disintegration and Integration
AbstractIn this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types. In the integration stage it recombines the units to memorize and recognize the target object effectively. With respect to the elementary trackers, we present a novel attentional feature-based correlation filter (AtCF) that focuses on distinctive attentional features. The effectiveness of the proposed algorithm is validated through experimental comparison with state-of-the-art methods on widely-used tracking benchmark datasets.
Related Material
[pdf] [supp][bibtex]@InProceedings{Choi_2016_CVPR,
author = {Choi, Jongwon and Chang, Hyung Jin and Jeong, Jiyeoup and Demiris, Yiannis and Choi, Jin Young},
title = {Visual Tracking Using Attention-Modulated Disintegration and Integration},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}
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