Correlation Filters Based on Strong Spatio-Temporal for Robust RGB-T Tracking

Futing LUO, Mingliang ZHOU*, Bing FANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


In this paper, we propose a strong spatio-temporal mechanism with correlation filters to solve multi-modality tracking tasks. First, we use the features of the previous four frames as spatio-temporal features, then aggregate the spatio-temporal features into the filters learning and positioning of the adjacent frame. Second, we enhance the temporal and spatial characteristics of the current frame filter by learning the previous four frame filters and spatial penalty. From the experimental results on the GTOT, VOT-TIR2019 and RGBT234 datasets, our strong spatio-temporal correlation filters has achieved excellent performance.

Original languageEnglish
Article number2250041
JournalJournal of Circuits, Systems and Computers
Issue number3
Early online date9 Sept 2021
Publication statusPublished - 1 Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 World Scientific Publishing Company.


  • correlation filters
  • RGB-T tracking
  • Spatio-temporal features


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