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Learning augmented memory joint aberrance repressed correlation filters for visual tracking

With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevert...

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Main Authors: Ji, Yuanfa, He, Jianzhong, Sun, Xiyan, Bai, Yang, Wei, Zhaochuan, Kamarul Hawari, Ghazali
Format: Article
Language:English
Published: MDPI 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/40157/1/Learning%20augmented%20memory%20joint%20aberrance%20repressed%20correlation.pdf
http://umpir.ump.edu.my/id/eprint/40157/
https://doi.org/10.3390/sym14081502
https://doi.org/10.3390/sym14081502
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spelling my.ump.umpir.401572024-02-07T07:19:52Z http://umpir.ump.edu.my/id/eprint/40157/ Learning augmented memory joint aberrance repressed correlation filters for visual tracking Ji, Yuanfa He, Jianzhong Sun, Xiyan Bai, Yang Wei, Zhaochuan Kamarul Hawari, Ghazali T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers. MDPI 2022-08 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40157/1/Learning%20augmented%20memory%20joint%20aberrance%20repressed%20correlation.pdf Ji, Yuanfa and He, Jianzhong and Sun, Xiyan and Bai, Yang and Wei, Zhaochuan and Kamarul Hawari, Ghazali (2022) Learning augmented memory joint aberrance repressed correlation filters for visual tracking. Symmetry, 14 (1502). pp. 1-19. ISSN 2073-8994. (Published) https://doi.org/10.3390/sym14081502 https://doi.org/10.3390/sym14081502
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Ji, Yuanfa
He, Jianzhong
Sun, Xiyan
Bai, Yang
Wei, Zhaochuan
Kamarul Hawari, Ghazali
Learning augmented memory joint aberrance repressed correlation filters for visual tracking
description With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.
format Article
author Ji, Yuanfa
He, Jianzhong
Sun, Xiyan
Bai, Yang
Wei, Zhaochuan
Kamarul Hawari, Ghazali
author_facet Ji, Yuanfa
He, Jianzhong
Sun, Xiyan
Bai, Yang
Wei, Zhaochuan
Kamarul Hawari, Ghazali
author_sort Ji, Yuanfa
title Learning augmented memory joint aberrance repressed correlation filters for visual tracking
title_short Learning augmented memory joint aberrance repressed correlation filters for visual tracking
title_full Learning augmented memory joint aberrance repressed correlation filters for visual tracking
title_fullStr Learning augmented memory joint aberrance repressed correlation filters for visual tracking
title_full_unstemmed Learning augmented memory joint aberrance repressed correlation filters for visual tracking
title_sort learning augmented memory joint aberrance repressed correlation filters for visual tracking
publisher MDPI
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/40157/1/Learning%20augmented%20memory%20joint%20aberrance%20repressed%20correlation.pdf
http://umpir.ump.edu.my/id/eprint/40157/
https://doi.org/10.3390/sym14081502
https://doi.org/10.3390/sym14081502
_version_ 1822924119510876160
score 13.232389

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