ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework
1 Shanghai Maritime University, Shanghai, China
ABSTRACT: Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.
Albrecht T, West G A, Tan T, et al. 2011. Visual maritime attention using multiple low-level features and naive bayes classification: Digital Image Computing Techniques and Applications (DICTA), International Conference on [C]. IEEE. - doi:10.1109/DICTA.2011.47
Bergamasco F, Benetazzo A, Barbariol F, et al. 2016. Multi-view horizon-driven sea plane estimation for stereo wave imaging on moving vessels[J]. Computers & Geosciences 95:105-117. - doi:10.1016/j.cageo.2016.07.012
Bolme D S , Beveridge J R , Draper B A , et al. 2010. Visual object tracking using adaptive correlation filters[J]. - doi:10.1109/CVPR.2010.5539960
Chen Weiqiang, Chen Jun, Zhang Wei, et al. 2016. Robust tracking control for ship heading adaptive neural network[J]. Journal of Ship Engineering (09): 15-20.
Chen Wenting, Liu Nantong, Ji Kefeng, et al. 2014. Ship Recognition for SAR Image Based on Multi-Classifier Fusion[J]. Remote Sensing Information (5): 90-95.
Chen Xiaojun, Yang Zhangqiong. 2017. Application of Support Vector Regression and Game Theory in Ship Moving Position Tracking [J].Ship science and technology (08):19-21.
He K , Zhang X , Ren S , et al. 2015. Deep Residual Learning for Image Recognition[J]. - doi:10.1109/CVPR.2016.90
Hong Z, Chen Z, Wang C, et al. 2015. Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, [C]. - doi:10.1109/CVPR.2015.7298675
Hong Z, Mei X, Prokhorov D, et al. 2013. Tracking via Robust Multi-task Multi-view Joint Sparse Representation[J]:649-656. - doi:10.1109/ICCV.2013.86
JIANG Shaofeng, WANG Chao, WU Fan, et al. 2014. COSMO-SkyMed Image Commercial Ship Classification Algorithm Based on Structural Feature Analysis[J]. Remote Sensing Technology and Application.29(4):607-615.
Johansson G. 1973. Visual perception of biological motion and a model for its analysis[J]. Perception & Psychophysics 14(2):201-211. - doi:10.3758/BF03212378
Kim D, Kim H, Jung S, et al. 2015. A vision-based detection algorithm for moving jellyfish in underwater environment: Ubiquitous Robots and Ambient Intelligence (URAI), 2015 12th International Conference on [C]. IEEE. - doi:10.1109/URAI.2015.7358846
Lecun Y, Bengio Y, Hinton G. 2015. Deep learning[J]. Nature 521(7553):436. - doi:10.1038/nature14539
Li X, Shang M, Hao J, et al. 2016. Accelerating fish detection and recognition by sharing CNNs with objectness learning: OCEANS 2016-Shanghai [C]. IEEE. - doi:10.1109/OCEANSAP.2016.7485476
Mayerschönberger V, Cukier K. 2014. Big data: A revolution that will transform how we live, work, and think.[J]. Mathematics & Computer Education 47(17):181-183. - doi:10.2501/IJA-33-1-181-183
Merchant N D, Witt M J, Blondel P, et al. 2012. Assessing sound exposure from shipping in coastal waters using a single hydrophone and Automatic Identification System (AIS) data[J]. Marine Pollution Bulletin 64(7):1320-1329. - doi:10.1016/j.marpolbul.2012.05.004
Moeslund T B, Granum E. 2001. A Survey of Computer Vision-Based Human Motion Capture[J]. Computer Vision & Image Understanding 81(3):231-268. - doi:10.1006/cviu.2000.0897
Robards M, Silber G, Adams J, et al. 2016. Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review[J] 92(1):75-103. - doi:10.5343/bms.2015.1034
Russell S J, Norvig P. 2010. Artificial intelligence: a modern approach[J]. Applied Mechanics & Materials 263(5):2829-2833.
Sang L, Wall A, Mao Z, et al. 2015. A novel method for restoring the trajectory of the inland waterway ship by using AIS data[J] 110:183-194. - doi:10.1016/j.oceaneng.2015.10.021
Shu Y, Daamen W, Ligteringen H, et al. 2017. Influence of external conditions and vessel encounters on vessel behavior in ports and waterways using Automatic Identification System data[J] 131:1-14. - doi:10.1016/j.oceaneng.2016.12.027
T'Jampens R, Hernandez F, Vandecasteele F, et al. 2016. Automatic detection, tracking and counting of birds in marine video content: Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on [C]. IEEE. - doi:10.1109/IPTA.2016.7821031
Xiao F, Han L, Gulijk C V, et al. 2015. Comparison study on AIS data of ship traffic behavior[J]. Ocean Engineering 95(3):84-93. - doi:10.1109/TCSVT.2015.2477937
Xu C, Lu C, Liang X, et al. 2016. Multi-loss Regularized Deep Neural Network[J]. IEEE Transactions on Circuits & Systems for Video Technology 26(12):2273-2283.
Zhang Z, Zhang X W, Liang R Y, et al. 2010. Research and Implementation of Ship-lock Monitoring System Based on SVM and Visual Perception[J]. Modern Electronics Technique. - doi:10.1109/TGRS.2016.2572736
Zou Z, Shi Z. 2016. Ship detection in spaceborne optical image with SVD networks[J]. IEEE Transactions on Geoscience and Remote Sensing 54(10):5832-5845.
Citation note:
Liu B., Wang S.Z., Xie Z.X., Zhao J.S., Li M.F.: Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 4, doi:10.12716/1001.13.04.01, pp. 699-705, 2019

Other publications of authors:

File downloaded 61 times

Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony