research-article

Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking

Authors:
Di Yuan

Harbin Institute of Technology, Shenzhen, China

Harbin Institute of Technology, Shenzhen, China
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,
Xiaojun Chang

RMIT University, Melbourne, VIC, Australia

RMIT University, Melbourne, VIC, Australia
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,
Zhihui Li

Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
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,
Zhenyu He

Harbin Institute of Technology, Shenzhen, China

Harbin Institute of Technology, Shenzhen, China
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ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18 Issue 3Article No.: 70pp 1–18https://doi.org/10.1145/3486678

Published:04 March 2022Publication History

ACM Transactions on Multimedia Computing, Communications, and Applications

Abstract

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.

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Index Terms

Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking

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Published in
ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 3
August 2022
478 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3505208
Editor:
Alberto Del Bimbo
University of Firenze, Italy
Issue’s Table of Contents
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Publication History
- Published: 4 March 2022
- Accepted: 1 September 2021
- Revised: 1 August 2021
- Received: 1 May 2021
Published in tomm Volume 18, Issue 3

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UAV tracking
discriminative correlation filters
adaptive spatial-temporal context-aware
Qualifiers
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Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking

ACM Transactions on Multimedia Computing, Communications, and Applications

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