Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking

Previous trackers mostly assume that the target has a smooth motion and perform target matching within a local window. However, targets often exhibit uncertain movements in real-world scenarios. Once the tracked target undergoes abrupt motion and moves outside the predefined local window, these trac...

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Published in:	Cluster computing Vol. 28; no. 1; p. 14
Main Authors:	Gao, Zeng, Zhuang, Yi, Gu, Jingjing
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Arithmetic Benchmarks Computer Communication Networks Computer Science Design Exploitation Global optimization Mathematical analysis Operating Systems Optical tracking Optimization algorithms Processor Architectures Qualitative analysis Sampling Uncertain motion tracking Arithmetic optimization algorithm Dynamic stratification strategy Multi-strategy
ISSN:	1386-7857, 1573-7543
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Abstract	Previous trackers mostly assume that the target has a smooth motion and perform target matching within a local window. However, targets often exhibit uncertain movements in real-world scenarios. Once the tracked target undergoes abrupt motion and moves outside the predefined local window, these trackers often fail. To address this issue, this paper introduces a multi-strategy arithmetic optimization algorithm (MSAOA) for global optimization and uncertain motion tracking. MSAOA is a high-performance optimizer that effectively solves uncertain motion in visual tracking. For MSAOA, we first design a dynamic stratification strategy to divide the population into three subpopulations. Then the mathematical model of each subpopulation is modified to improve the exploration and exploitation performance. Finally, extensive experiments over 23 benchmark functions and CEC2020 benchmark problems show that MSAOA is better than other algorithms. For the MSAOA tracker (MSAOAT), we utilize the proposed MSAOA as a joint local sampling-global search to generate candidate targets and match the best targets by a fitness function. More importantly, we design a verifier to unite local sampling and global search to form a complete tracking framework, which can effectively address smooth and abrupt motion in visual tracking. The qualitative and quantitative analyses on the general motion group and the abrupt motion group demonstrate that the MSAOAT can outperform other trackers.
AbstractList	Previous trackers mostly assume that the target has a smooth motion and perform target matching within a local window. However, targets often exhibit uncertain movements in real-world scenarios. Once the tracked target undergoes abrupt motion and moves outside the predefined local window, these trackers often fail. To address this issue, this paper introduces a multi-strategy arithmetic optimization algorithm (MSAOA) for global optimization and uncertain motion tracking. MSAOA is a high-performance optimizer that effectively solves uncertain motion in visual tracking. For MSAOA, we first design a dynamic stratification strategy to divide the population into three subpopulations. Then the mathematical model of each subpopulation is modified to improve the exploration and exploitation performance. Finally, extensive experiments over 23 benchmark functions and CEC2020 benchmark problems show that MSAOA is better than other algorithms. For the MSAOA tracker (MSAOAT), we utilize the proposed MSAOA as a joint local sampling-global search to generate candidate targets and match the best targets by a fitness function. More importantly, we design a verifier to unite local sampling and global search to form a complete tracking framework, which can effectively address smooth and abrupt motion in visual tracking. The qualitative and quantitative analyses on the general motion group and the abrupt motion group demonstrate that the MSAOAT can outperform other trackers.
ArticleNumber	14
Author	Gu, Jingjing Gao, Zeng Zhuang, Yi
Author_xml	– sequence: 1 givenname: Zeng surname: Gao fullname: Gao, Zeng organization: The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 2 givenname: Yi surname: Zhuang fullname: Zhuang, Yi email: nuaa317@163.com organization: The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 3 givenname: Jingjing surname: Gu fullname: Gu, Jingjing organization: The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
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SubjectTerms	Accuracy Algorithms Arithmetic Benchmarks Computer Communication Networks Computer Science Design Exploitation Global optimization Mathematical analysis Operating Systems Optical tracking Optimization algorithms Processor Architectures Qualitative analysis Sampling
Title	Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking
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