@inproceedings{1ba2f211e979437cb1d56ace923ef6fc,
title = "Learning Variance Kernelized Correlation Filters for Robust Visual Object Tracking",
abstract = "Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable features and kernel functions incorporated, can give very promising tracking results. In this paper, we propose to learn discriminative correlation filters that incorporate information from the variances of the target's appearance features. We have evaluated our filters against several recent tracking methods on the OTB benchmark dataset. Our results show that the additional feature variances help to improve the robustness of the correlation filters in complex scenes.",
author = "Chenghuan Liu and Du Huynh and Mark Reynolds",
year = "2017",
month = dec,
day = "19",
doi = "10.1109/DICTA.2017.8227458",
language = "English",
series = "DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--8",
editor = "Yi Guo and Manzur Murshed and Zhiyong Wang and Feng, {David Dagan} and Hongdong Li and Cai, {Weidong Tom} and Junbin Gao",
booktitle = "2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)",
address = "United States",
note = "2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA ; Conference date: 29-11-2017 Through 01-12-2017",
}