Shape-Based Analysis for Vessel Trajectories
Jiang Wang, Yun Zhou, Xiaofeng Cao, Yilin Wang, Cheng Zhu, Weiming Zhang · 2017
In this paper we propose a novel method for modeling the shape of vessel trajectories in a manner which may facilitate the application of machine learning techniques. This is achieved by transforming the topological feature of vessel trajectories into vectors. More specifically, we calculate scale-invariance indicators for every vessel trajectory as shape characteristics, and other indicators to denote the trajectory area. The proposed method is validated using both synthetic trajectories and real-world AIS datasets. We demonstrate that it can achieve good time efficiency and may support vessel trajectory related analysis.