Long-term detection and tracking algorithm for moving vessels by maritime UAVs

被引：0
|
作者：
Fan Y. [1 ,2 ]
Zhang K. [1 ,2 ]
Niu L. [1 ,2 ]
Liu T. [1 ,2 ]
Fei F. [1 ,2 ]
机构：
[1] College of Marine Electrical Engineering, Dalian Maritime University, Dalian
[2] Key Laboratory of Technology and System for Intelligent Ships of Liaoning Province, Dalian
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2024年 / 45卷 / 03期
关键词：
correlation filtering; long-term tracking; maritime UAVs; re-detection; rotate tracking box;
D O I：
10.19650/j.cnki.cjsi.J2312127
中图分类号：
学科分类号：
摘要：
An algorithm for long-term maritime target detection and tracking based on the combination of YOLOv5 and ECO_HC is proposed to address the problem of tracking failure caused by occlusion of ship hulls and ships leaving the field of view during unmanned aerial vehicle (UAV) tracking of ship at sea. First, perceptual hashing and the ratio between the second and first major modes are used to comprehensively assess the reliability of the tracking process. In the event of target loss, the YOLOv5 detector is utilized to reposition the target and initialize the tracking model. Thereby, the accumulation of erroneous information is eliminated. Secondly, to address the rotational changes of the target during tracking, the Fourier-Mellin transform is employed for rotation parameter estimation, mitigating performance decline due to target rotation. The proposed algorithm achieves an average precision and success rate of 83.9% and 76.7%, respectively, on the OTB-100 dataset. Field experiments of ship tracking in actual maritime scenarios on UAV platforms show precision and success rates of 80.9% and 60.4% under complete occlusion, and 90.2% and 48.3% when the target is out of the field of view. The experiments demonstrate that the proposed algorithm can effectively suppress the influence of common maritime interference factors. © 2024 Science Press. All rights reserved.
引用
收藏
页码：326 / 335
页数：9
相关论文
共 28 条
  • [1] LI W, GE Y, GUAN Z, Et al., Synchronized motion-based UAV-USV cooperative autonomous landing, Journal of Marine Science and Engineering, 10, 9, (2022)
  • [2] WANG Y, LIU W, LIU J, Et al., Cooperative USV-UAV marine search and rescue with visual navigation and reinforcement learning-based control [J], ISA Transactions, (2023)
  • [3] HAN R Z, FENG W, GUO Q, Et al., Single object tracking research: A survey, Chinese Journal of Computer, 45, 9, pp. 1877-1907, (2022)
  • [4] HENRIQUES J F, CASEIRO R, MARTINS P, Et al., High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 3, pp. 583-596, (2015)
  • [5] KIANI GALOOGAHI H, FAGG A, LUCEY S., Learning background-aware correlation filters for visual tracking, Proceedings of the IEEE International Conference on Computer Vision, pp. 1135-1143, (2017)
  • [6] FU C, LI B, DING F, Et al., Correlation filters for unmanned aerial vehicle-based aerial tracking: A review and experimental evaluation, IEEE Geoscience and Remote Sensing Magazine, 10, 1, pp. 125-160, (2021)
  • [7] DANELLJAN M, BHAT G, SHAHBAZ KHAN F, Et al., Eco: Efficient convolution operators for tracking, IEEE International Conference on Computer Vision and Pattern Recognition, pp. 6638-6646, (2017)
  • [8] LI F, TIAN C, ZUO W, Et al., Learning spatial-temporal regularized correlation filters for visual tracking, IEEE International Conference on Computer Vision and Pattern Recognition, pp. 4904-4913, (2018)
  • [9] FU C, LIN F, LI Y, Et al., Correlation filter-based visual tracking for UAV with online multi-feature learning, Remote Sensing, 11, 5, (2019)
  • [10] LI Y, FU C, DING F, Et al., AutoTrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization, IEEE Conference on Computer Vision and Pattern Recognition, pp. 11923-11932, (2020)
123