Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers With Superior IR Performance

Erhan Gundogdu, Huseyin Ozkan, H. Seckin Demir, Hamza Ergezer, Erdem Akagunduz, S. Kubilay Pakin; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 1-9

Abstract


The subject of this paper is the object tracking problem in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner. We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery.

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[bibtex]
@InProceedings{Gundogdu_2015_CVPR_Workshops,
author = {Gundogdu, Erhan and Ozkan, Huseyin and Seckin Demir, H. and Ergezer, Hamza and Akagunduz, Erdem and Kubilay Pakin, S.},
title = {Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers With Superior IR Performance},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2015}
}