Surrounding-aware correlation filter for UAV tracking with selective spatial regularization

The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have drawn great attention and undergone remarkable progre...

Full description

Saved in:
Bibliographic Details
Main Authors: Fu, Changhong, Xiong, Weijiang, Lin, Fuling, Yue, Yufeng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/154886
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-154886
record_format dspace
spelling sg-ntu-dr.10356-1548862022-01-13T02:49:30Z Surrounding-aware correlation filter for UAV tracking with selective spatial regularization Fu, Changhong Xiong, Weijiang Lin, Fuling Yue, Yufeng School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Unmanned Aerial Vehicle Visual Object Tracking The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have drawn great attention and undergone remarkable progress. However, background interference and boundary effect remain two thorny problems. In this paper, a surrounding-aware tracker with selective spatial regularization (SASR) is presented. SASR tracker extracts surrounding samples according to the size and shape of the object in order to utilize context and maintain the integrality of the object. Additionally, a selective spatial regularizer is introduced to address boundary effect. Central coefficients in the filter are evenly regularized to preserve valid information from the object. While the others are penalized according to their spatial location. Under the framework of SASR tracker, surrounding information and selective spatial regularization prove to be complementary to each other, which actually did not draw much attention before. They managed to improve not only the robustness against various distractions in the surrounding but also the flexibility to catch up with frequent appearance change of the object. Qualitative evaluation and quantitative experiments on challenging UAV tracking sequences have shown that SASR tracker has performed favorably against 23 state-of-the-art trackers. The work was supported by the National Natural Science Fundation of China (no. 61806148) and the Fundamental Research Funds for the Central Universities (no.22120180009). 2022-01-13T02:49:30Z 2022-01-13T02:49:30Z 2020 Journal Article Fu, C., Xiong, W., Lin, F. & Yue, Y. (2020). Surrounding-aware correlation filter for UAV tracking with selective spatial regularization. Signal Processing, 167, 107324-. https://dx.doi.org/10.1016/j.sigpro.2019.107324 0165-1684 https://hdl.handle.net/10356/154886 10.1016/j.sigpro.2019.107324 2-s2.0-85072997286 167 107324 en Signal Processing © 2019 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Unmanned Aerial Vehicle
Visual Object Tracking
spellingShingle Engineering::Electrical and electronic engineering
Unmanned Aerial Vehicle
Visual Object Tracking
Fu, Changhong
Xiong, Weijiang
Lin, Fuling
Yue, Yufeng
Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
description The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have drawn great attention and undergone remarkable progress. However, background interference and boundary effect remain two thorny problems. In this paper, a surrounding-aware tracker with selective spatial regularization (SASR) is presented. SASR tracker extracts surrounding samples according to the size and shape of the object in order to utilize context and maintain the integrality of the object. Additionally, a selective spatial regularizer is introduced to address boundary effect. Central coefficients in the filter are evenly regularized to preserve valid information from the object. While the others are penalized according to their spatial location. Under the framework of SASR tracker, surrounding information and selective spatial regularization prove to be complementary to each other, which actually did not draw much attention before. They managed to improve not only the robustness against various distractions in the surrounding but also the flexibility to catch up with frequent appearance change of the object. Qualitative evaluation and quantitative experiments on challenging UAV tracking sequences have shown that SASR tracker has performed favorably against 23 state-of-the-art trackers.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Fu, Changhong
Xiong, Weijiang
Lin, Fuling
Yue, Yufeng
format Article
author Fu, Changhong
Xiong, Weijiang
Lin, Fuling
Yue, Yufeng
author_sort Fu, Changhong
title Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
title_short Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
title_full Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
title_fullStr Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
title_full_unstemmed Surrounding-aware correlation filter for UAV tracking with selective spatial regularization
title_sort surrounding-aware correlation filter for uav tracking with selective spatial regularization
publishDate 2022
url https://hdl.handle.net/10356/154886
_version_ 1722355381846409216