A Hybrid Excitation Model Based Lightweight Siamese Network for Underwater Vehicle Object Tracking Missions

Wu, Xiaofeng; Han, Xinyue; Zhang, Zongyu; Wu, Haoyu; Xu, Yang; Huang, Hai

doi:10.3390/jmse11061127

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Wang et al [8] improved the kernel correlation filter (KCF) by using a dynamic continuous change scale and adaptive filter update strategy which can better predict the position of an underwater target; better tracking effects can be achieved with this improvement. Faced with the complexity of the underwater environment, Wu et al [9] presented an improved Siamese network which introduced a lightweight network and hybrid excitation model to reduce the computational complexity and enhance the network's accuracy to achieve better underwater target tracking. Hong et al [10] proposed an improved YOLOv4 algorithm that simplifies the feature extraction layer network and uses a residual network instead of continuous convolution operation, thereby improving the poor real-time operation and low accuracy of multi-ship target tracking.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Yao

Yang

2023

JMSE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Yang

Yao

Yang

2023

JMSE

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Underwater object tracker: UOSTrack for marine organism grasping of underwater vehicles

Wang

et al. 2023

Ocean Engineering

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ULOTrack: Underwater Long-Term Object Tracker for Marine Organism Capture

He,

Yu,

Wei

et al. 2024

JMSE

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Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed.

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A Hybrid Excitation Model Based Lightweight Siamese Network for Underwater Vehicle Object Tracking Missions

Cited by 3 publications

References 31 publications

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

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

Underwater object tracker: UOSTrack for marine organism grasping of underwater vehicles

ULOTrack: Underwater Long-Term Object Tracker for Marine Organism Capture

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