@inproceedings{09ed73bb13e74890b0b1d3d9540f8d69,
title = "A scale adaptive kernel correlation filter tracker with feature integration",
abstract = "Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72\% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.",
keywords = "Correlation filter, Kernel learning, Visual tracking",
author = "Yang Li and Jianke Zhu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
year = "2015",
doi = "10.1007/978-3-319-16181-5\_18",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "254--265",
editor = "Carsten Rother and Bronstein, \{Michael M.\} and Lourdes Agapito",
booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",
address = "德国",
}