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- W4385202276 abstract "Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results." @default.
- W4385202276 created "2023-07-25" @default.
- W4385202276 creator A5015964434 @default.
- W4385202276 creator A5018616205 @default.
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- W4385202276 date "2023-07-21" @default.
- W4385202276 modified "2023-09-28" @default.
- W4385202276 title "Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking" @default.
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- W4385202276 doi "https://doi.org/10.3390/jmse11071456" @default.
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