A Fast Hyperspectral Object Tracking Method Based on Channel Selection Strategy

Yifan Zhang, Xu Li, Feiyue Wang, Baoguo Wei, Lixin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

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.

Original languageEnglish
Title of host publication2022 12th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665470698
DOIs
StatePublished - 2022
Event12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 - Rome, Italy
Duration: 13 Sep 202216 Sep 2022

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2022-September
ISSN (Print)2158-6276

Conference

Conference12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022
Country/TerritoryItaly
CityRome
Period13/09/2216/09/22

Keywords

  • channel selection
  • Hyperspectral video
  • object tracking

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