دورية أكاديمية

Selective part‐based correlation filter tracking algorithm with reinforcement learning

التفاصيل البيبلوغرافية
العنوان: Selective part‐based correlation filter tracking algorithm with reinforcement learning
المؤلفون: Zhengzhi Lu, Guoan Yang, Deyang Liu, Junjie Yang, Yong Yang, Chuanbo Zhou
المصدر: IET Image Processing, Vol 16, Iss 4, Pp 1208-1226 (2022)
بيانات النشر: Wiley, 2022.
سنة النشر: 2022
المجموعة: LCC:Computer software
مصطلحات موضوعية: Photography, TR1-1050, Computer software, QA76.75-76.765
الوصف: Abstract In visual object tracking methods, improving both the run time and the accuracy in the face of complex situations has always been an important issue. Many complex tracking algorithms, such as part‐based algorithms, have better accuracy when facing occlusions, but they have much greater computational complexity. In response to the above problems, this paper proposes a selective part‐based correlation filter (SPCF) tracking algorithm with a reinforcement learning to achieve more stable and efficient tracking of targets. First, according to the conditions of the response map of the correlation filter (CF), the entire tracking process is divided into three states: simple environments, complex environments, and harsh environments. Second, this paper uses reinforcement learning to determine the states of frames in different situations to improve the tracking effect of the algorithm. Third, the process of the online selection of states is transformed into a Markov decision process (MDP), where the policy learning of the MDP is achieved by reinforcement learning. Additionally, different strategies are used to track a target in different states: the overall filter is used to increase the speed in simple environments; part‐based filters are used to improve the accuracy in complex environments; and in harsh environments where the target completely disappears, a redetection algorithm is used to find the target when it reappears. Finally, the performance of the tracking algorithm is verified on the VOT2018, OTB‐2015, and LaSOT datasets.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1751-9667
1751-9659
Relation: https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667
DOI: 10.1049/ipr2.12405
URL الوصول: https://doaj.org/article/1491e543c81a4f6eb1e5af98372e7bc1
رقم الأكسشن: edsdoj.1491e543c81a4f6eb1e5af98372e7bc1
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:17519667
17519659
DOI:10.1049/ipr2.12405