Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 256-260.doi: 10.11896/jsjkx.211100253

• Big Data & Data Science • Previous Articles     Next Articles

SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion

YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng   

  1. School of Computer Science and Technology,Hainan University,Haikou 570228,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YU Shu-hao,born in 1996,postgra-duate.His main research interests include artificial intelligence and data mining.
    ZHOU Hui,born in 1980,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include artificial intelligence and sensor networks.
  • Supported by:
    National Science Foundation of China(61962017),Hainan Provincial Key Research and Development Program(ZDYF2020018) and National Key Research and Development Program(2018YFB2100805).

Abstract: With the rapid development of ocean transportation,the technology of vessel trajectory mining and analysis has become more and more important.Trajectory clustering has many practical applications in the ship field,such as anomaly detection,position prediction,ship collision avoidance and so on.Traditional trajectory similarity calculation methods are relatively low in accuracy and efficiency,and most existing deep learning methods only extract features of static ones,ignoring the multi-feature combination of dynamic and static features.In order to solve the problem,a static-dynamic-feature fusion model based on convolutional auto-encoder is proposed,which can extract more perfect trajectory features.It makes up for the deficiency of multi-feature fusion technique in vessel trajectory clustering.Experiments on real datasets have demonstrated that compared with traditional methods such as LCSS,DTW and multi-feature extraction model based on deep learning,the proposed model has at least 5%~10% improvement in metrics such as precision,accuracy and so on.

Key words: Convolutional auto-encoder (CAE), Multi-feature fusion, Ship automatic identification system (AIS), Trajectory clustering

CLC Number: 

  • TP391
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