Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

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.

Automated summary

Due to the complexity of the underwater environment, traditional particle filters face challenges in tracking underwater targets. This paper proposes a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) to improve the tracking accuracy. It addresses the problem of underwater target feature construction and scale transformation, and introduces a corrected background-weighted histogram method for feature recognition and a scale filter for target scaling transformation. Additionally, a nonlinear escape energy is constructed using the Harris hawks algorithm to balance exploration and exploitation processes for faster computational speed. Experimental results show that the proposed HHOPF tracker provides better tracking results compared to other algorithms.