@inproceedings{4d4a21003b794306b1fb725840a1e63e,
title = "A Fast Hyperspectral Object Tracking Method Based on Channel Selection Strategy",
abstract = "Hyperspectral object tracking aims to take advantage of the rich spatial and spectral information in hyperspectral videos to effectively improve the robustness and accuracy of object tracking. Compared with color videos, hyperspectral videos have huge amount of data bringing a challenge to the efficiency of object tracking. We propose a fast hyperspectral object tracking method based on channel selection strategy. The strategy considers the spatial and spectral changes of local regions in the frame image and selects only three channels fed to tracker to speed up. The experimental results show that our method reaches 11.5 FPS on the dataset of Hyperspectral Object Tracking Challenge, which is faster than the state-of-the-art methods.",
keywords = "channel selection, Hyperspectral video, object tracking",
author = "Yifan Zhang and Xu Li and Feiyue Wang and Baoguo Wei and Lixin Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 ; Conference date: 13-09-2022 Through 16-09-2022",
year = "2022",
doi = "10.1109/WHISPERS56178.2022.9955094",
language = "英语",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2022 12th Workshop on Hyperspectral Imaging and Signal Processing",
}