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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.
Rocznik
Tom
Strony
699--705
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
- Shanghai Maritime University, Shanghai, China
autor
- Shanghai Maritime University, Shanghai, China
autor
- Shanghai Maritime University, Shanghai, China
autor
- Shanghai Maritime University, Shanghai, China
autor
- Shanghai Maritime University, Shanghai, China
Bibliografia
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- 3. 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
- 4. 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.
- 5. 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.
- 6. 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.
- 7. He K , Zhang X , Ren S , et al. 2015. Deep Residual Learning for Image Recognition[J]. - doi:10.1109/CVPR.2016.90
- 8. 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
- 9. 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
- 10. 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.
- 11. 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
- 12. 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
- 13. Lecun Y, Bengio Y, Hinton G. 2015. Deep learning[J]. Nature 521(7553):436. - doi:10.1038/nature14539
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. 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
- 19. Russell S J, Norvig P. 2010. Artificial intelligence: a modern approach[J]. Applied Mechanics & Materials 263(5):2829-2833.
- 20. 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
- 21. 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
- 22. 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
- 23. 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
- 24. 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.
- 25. 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
- 26. 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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