Robust visual tracking via modified Harris hawks optimization

Yuqi Xiao

doi:10.1016/j.imavis.2024.104959

2024-04-01

PRE

Yuqi Xiao *

吴

吴勇军 (Yongjun Wu)

DOI：10.1016/j.imavis.2024.104959

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摘要

En 中文

Due to its outstanding efficiency and high precision, Harris hawks optimization (HHO for short) is suitable for solving the problem of visual target tracking under conditions of occlusion, deformation, rotation and in other complicated tracking scenes. A visual target tracker based on HHO is proposed in this study. To further promote the efficiency and stability of the standard HHO method and reduce the probability of the iteration falling into local optima and algorithm prematurity, in this study we propose an improved method called Super-HHO and apply it to visual target tracking. Compared with standard HHO, Super-HHO is superior due to its parameter optimization and updating strategy. We first optimize the random parameters of HHO via chaos theory to avoid frequent repeated exploration of the feasible region. Next, we design a nonlinear renewal strategy for the escape energy, which solves the problem in traditional HHO in which the fixed escape energy cannot accurately reflect the real hunting process of Harris hawks. Mutation strategies are also designed for the locations of the prey and the hunters to improve the optimization ability and eliminate the risk of falling into local extremes. In addition, a frame scale adjustment method model is developed to address the the issue in which the use of a size-fixed tracking frame makes it easy to include too many invalid features, which reduces the efficiency. Finally, we use the OTB2015, and VOT2018 tracking evaluation datasets, which contain hundreds of visual sequences and more than 10 complex interference scenes to conduct a qualitative analysis, a quantitative analysis and a statistical analysis of ours and other classic trackers, and to effectively test and compare the success ratio, precision and stability of each tracker. The proposed method was also compared with other classic trackers using classic large-scale benchmarks such as LaSOT and TrackingNet. Experimental data prove that ours performs well in terms of robustness, precision and efficiency.

Keyword:

Frame scale adaptive adjustment

Harris hawks optimization

Generative visual tracker

Computer vision technology