Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum

Abstract

Correlation filters for visual object tracking in visible imagery has been well-studied. Most of the correlation-filterbased methods use either raw image intensities or feature maps of gradient orientations or color channels. However, well-known features designed for visible spectrum may not be ideal for infrared object tracking, since infrared and visible spectra have dissimilar characteristics in general. We assess the performance of two state-of-the-art correlationfilter-based object tracking methods on Linköping Thermal InfraRed (LTIR) dataset of medium wave and longwave infrared videos, using deep convolutional neural networks (CNN) features as well as other traditional hand-crafted descriptors. The deep CNN features are trained on an infrared dataset consisting of 16K objects for a supervised classification task. The highest performance in terms of the overlap metric is achieved when these deep CNN features are utilized in a correlation-filter-based tracker.

DOI: 10.1109/CVPRW.2016.43

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Cite this paper

@article{Gundogdu2016EvaluationOF, title={Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum}, author={Erhan Gundogdu and Aykut Koc and Berkan Solmaz and Riad I. Hammoud and A. Aydin Alatan}, journal={2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, year={2016}, pages={290-298} }