A Survey on Tracking and Development of Marine Targets

10.6919/ICJE.202205_8(5).0066

Kai Chen

Target Tracking ； Multi-modality ； Image Fusion ； Deep Learning

International Core Journal of Engineering

8卷5期（2022 / 05 / 01）

514 - 521

英文

The 21st century is the era of big data and the era of ocean development. China is rich in marine resources. For this reason, China has put forward the goal of building a marine power. Marine target tracking is an important part of the development of the ocean. However, the current mainstream target tracking algorithms are mainly used in land target tracking research. In this paper, the relevant researches on marine target tracking are reviewed, and the focus is on and analysis of marine multimodal target tracking.

1. Henriques J F, Caseiro R, Martins P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(3):583-596.
   連結：
2. Martin, Danelljan, Gustav, et al. Discriminative Scale Space Tracking [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39 (8): 1561-1575.
   連結：
3. Danelljan M, Hger G, Khan F S, et al. Learning Spatially Regularized Correlation Filters for Visual Tracking [J]. IEEE International Conference on Computer Vision (ICCV), 2016: 4310-4318.
   連結：
4. ZHANG Y, LI Q Z, ZANG F N, Ship detection for visual maritime surveillance from non-stationary platforms[J]. Ocean Engineering, 2017, 141:53-53. DOL:10.10106/j.oceaneng.2017.06.022.
   連結：
5. KONG X, LIU L, QIAN Y, et al. Automatic detection of sea-sky horizon line and small targets in maritime infrared imagery[J]. Infrared Physics & Technology, 2016, 76:185-199.
   連結：
6. Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11):2278.
   連結：
7. Mikolov T, Karafiát M, Burget L, et al. Recurrent neural networkbased language model // Eleventh Annual Conference of the International Speech Communication Association. Makuhari, 2010: 1045.
   連結：
8. Hinton G E. Deep belief networks[J/OL]. Scholarpedia (2009-04-11) [2019-03-05]. http://www. scholarpedia.org/article/Deep_belief_networks.
   連結：
9. Bertinetto L, Valmadre J, Henriques JF, et al. Fully-Convolutional Siamese Networks for Object Tracking [C]/ European Conference on Computer Vision, 2016. https://doi.org/10.1007/978-3 -319-48881-3_56.
   連結：
10. Bo L, Yan J , Wei W , et al. High Performance Visual Tracking with Siamese Region Proposal Network[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018.
    連結：
11. Hao Yue, Lilun Zhang, Dezhi Wang, Yongxian Wang, Zengquan Lu. The Classification of Underwater Acoustic Targets Based on Deep Learning Methods[A]. Science and Engineering Research Center. Proceedings of 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI2017)[C].Science and Engineering Research Center:Science and Engineering Research Center, 2017:4.
    連結：
12. Yang Honghui, Shen Sheng, Yao Xiaohui, Sheng Meiping, Wang Chen. Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition. [J]. Sensors (Basel, Switzerland), 2018, 18(4).
    連結：
13. BOUSETOUANE, MORRIS B, Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios[C] / 2016 13th IEEE International Conerence on Advanced Video and Signal Based Sueveillance(AVSS). IEEE,2016:242-248.
    連結：
14. Xu Weiming, Liu Yanchun, Yin Xiaodong. Underwater target tracking method based on interactive multi-model [J]. Geo-Spatial Information Science, 2008(03): 186-190.
    連結：
15. Liu Huaping, Wang Feng, Zhang Xinyu, et al.Weaklypaired deep dictionary learning for cross-modal retrieval[J].Pattern Recognition Letters, 2018,3(10):66-73.
    連結：
16. Jun Song, Zhongben Zhu, and Lei Wan. 2019. Surface Multi-target Tracking Algorithm Based on Data Association of Information Fusion. In Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering(CACRE2019). Association for Computing Machinery, NewYork, NY, USA, Article 10, 1–6. DOI: https://doi.org/10.1145/3351917.3351974.
    連結：
17. Sheng, X.; Chen, Y.; Guo, L.; Yin, J.; Han, X. Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks. Sensors 2018, 18, 3193.
    連結：
18. Tharindu Fernando, Simon Denman, Sridha Sridharan, et al. An LSTM framework for human trajectory prediction and abnormal event detection[J]. Neural Networks,2018,108:466-478.
    連結：
19. Lan Ma, Shan Tian. A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction[J]. Access, 2020, 8:134668-134680.
    連結：
20. Zhou T T, Jing H L, Sun H. Reliability, validity and development of ocean thermal energy conversion [C]//Proceedings of the World Automation Congress.Puerto Vallarta,Mexico,2012:1-5.
21. Tang Yiming, Liu Yufei, Huang Hong. Overview of Visual Single Target Tracking Algorithms [J]. Measurement and Control Technology, 2020, 39 (08): 21-34. (Tang Yiming, Liu.
22. Shi Guixin, Yan Shefeng, Liu Yu. Improved nonlinear filtering fast algorithm for underwater target tracking [J]. Applied Acoustics, 2020, 39(01): 89-96.
23. Ding Yi, Zhang Yao, Li Guannan. Application of Neural Network Corrected EKF in Underwater Passive Target Tracking [J]. Small Microcomputer System, 2020, 41(05): 897-901.
24. Zhao Wentao, Cao Xinyu, Tian Zhiyong. Infrared ship target segmentation method based on adaptive threshold region growth [J]. Infrared Technology, 2018(2):158-163.
25. Wang Guanqun, Zhang Chunhua, Yin Li, Li Yu, Zhang Yangfan. Underwater multi-target tracking with improved joint multi-station multi-Bernoulli filter [J]. Chinese Journal of Acoustics, 2021, 46(04): 508-518. DOI: 10.15949/j.cnki.0371-0025.2021.04.003.
26. Li Chen, Huang Zhaoqiong, Xu Ji, Guo Xinyi, Gong Zaixiao, Yan Yonghong. Multi-channel underwater target recognition using deep learning [J]. Chinese Journal of Acoustics, 2020, 45(04): 506-514.
27. Wang Qiang, Zeng Xiangyang. Deep learning method and its application in underwater target recognition [A]. Hydroacoustics Branch of the Acoustical Society of China. Proceedings of the 2015 Academic Conference of the Hydroacoustics Branch of the Acoustical Society of China [C]. China Acoustics Hydroacoustics Branch of the Society: Acoustical Society of China, 2015: 3.
28. Wang Qiang, Zeng Xiangyang. Deep learning method and its application in underwater target recognition [C]//. Proceedings of the 2015 Academic Conference of the Hydroacoustics Branch of the Acoustical Society of China. [Publisher unknown], 2015:148- 150.
29. Chen Guojun, Chen Wei, Yu Hanqi. Research on the target tracking method of monocular vision underwater robot based on deep learning [J]. Machine Tools and Hydraulics, 2019, 47(23): 79-82.
30. Xu Jianhua, Dou Yigeng, Zheng Yashan. An underwater target recognition and tracking method based on YOLO-V3 algorithm [J]. Chinese Journal of Inertial Technology, 2020, 28(01): 129-133.
31. Wang Yujie, Li Yu, Ju Donghao, Huang Haining. A Multi-target Passive Tracking Algorithm Based on Underwater Unmanned Vehicle [J]. Journal of Electronics and Information, 2020, 42(08): 2013-2020.
32. Zhang Qingguo, Huang Qipei, Yao Qingsheng, Lian Li. Research on underwater multi-target positioning and tracking technology in the deep sea [J]. Electronic Measurement Technology, 2019, 42(22): 67-72.
33. Hu Yuke, Xia Wei, Hu Xiaoxuan, et al. Ship Track Prediction Based on Recurrent Neural Network [J]. Systems Engineering and Electronic Technology, 2020, 42(4): 871-877.
34. Zeng Sai, Cheng Jinsheng. Application of Heterogeneous Multimodal Deep Learning Method in Underwater Target Recognition [C]//. Proceedings of the 2018 National Acoustics Conference C Underwater Acoustic Engineering and Underwater Acoustic Signal Processing. [Published Unknown], 2018:76-77.
35. Xu Shuxian, Zhao Zhimei. High-precision infrared and visible light video moving target intelligent tracking method [J]. Laser Journal, 2021, 42(08): 192-195. DOI: 10.14016/j.cnki.jgzz.2021.08.192.
36. Jiang Shan, Di Xiaoqiang, Han Cheng. Visual tracking of twin network with fusion of spatiotemporal characteristics [J/OL]. Journal of Ordnance Engineering, 2021, 1-11.
37. Sun Jiongning, Lu Taizhi, Zhang Juan, Guo Haitao. Target tracking method integrating deep learning and spatiotemporal prediction [J]. Radio Engineering, 2021,51(09):909-914.
38. Wang Ling, Zhou Lei, Wang Peng, Bai Yan'e. Research on tracking algorithm based on attention mechanism and twin network [J/OL]. Computer Engineering and Applications, 2021, 1-11.
39. ZHANG Yun-fei, HUANG Run-hui, SHAN Yun-xiao, ZHOU Xiao-mei. A high robust real-time single-target ship tracking method based on siamese network. Ship Science and Technology, 2019, 41(12 ): 103-108.
40. Li Xiaohua, Su Jun, Li Xiuxiu. Single-observation station underwater pure azimuth multi-target tracking in strong interference environment [J/OL]. Computer Engineering and Applications: 1-8 [2020-12-22].