Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Development of A Vision-based Ship Behavior Characteristics Extraction Approach and Comparison
with K-means Clustering
Zhiyuan CAI, Long YU, Qidong FAN, Lecheng LI
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering School of Naval
Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University.
Shanghai, China

sailing environmental conditions and the captain's operationconsiderations. This is also one of the biggest differences between theactual ship data and the experimental data. The experimental data isconnected with the sailing conditions and ship operation thoughts while
the actual ship data is not, so it is impossible to tell what the ship wasdoing at that time, whether it was berthing,maneuvering, or sailingstraight from the data.
Dealing with massive real ship data, people try to use statistics, machinelearning and other methods to explore ship characteristics. A largenumber of scholars have carried out research on ship behavior
recognition based on real ship data. Scholars who studying ships mainly
analyze macroscopic behaviors such as ship area density and navigationroute based on trajectory data, and use specific feature extraction
methods and clustering algorithms to classify ships’ behaviors in acertain area (Zhou, Daamen, Vellinga and Hoogendoorn, 2019). Somescholars combine ship maneuvering and trajectory data to analyze
microscopic ship maneuvering behaviors such as ship acceleration andlarge rudder angle turning (Gao and Shi, 2020). These studies directly
identify the ship behavior of the real ship data, fragment the complex real
ship data, and mark the corresponding maneuvering behavior. People'sattention is mainly focused on the research of macro shipping behavior
and micro ship handling behavior, but there are few studies on analyzing
the ship's navigation state, reproducing the captain's ship handlingthought, and establishing a bridge connecting the ship's macro behavior
and micro behavior.Since the data is separated from the navigation
conditions and the decision of the ship operators, using a large amountof track data training model to predict the possible future position andstate of the ship, can only predict trajectories in a short time, and theaccuracy of long-time prediction is poor (Scheepens, van de Weteringand van Wijk, 2014; Simsir, Amasyalı, Bal, Çelebi and Ertugrul, 2014;
Zhou, Chen and Zhang, 2019). Therefore, it is necessary to use real shipdata to conduct intelligent exploration of ships and to indicate thecaptain's thoughts from the data.
Real ship big data mining needs to rely on machine learning big dataanalysis technology, and feature extraction is an important part ofmachine learning methods, which usually needs to use the priorknowledge of ship operation. However, due to the lack of practical ship
operation experience, data analysts can not effectively describe ship
operation ideas, and the logical connection between real ship data canABSTRACT
The poor quality of real ship data and the lack of navigation status marks
make it different from the experimental data recorded with experimental
conditions, which is difficult to be effectively used in the study of ship
performance. In this paper, a visual analysis system of navigation logic
is developed to assist in abnormal data processing. At the same time, thevisual analysis is used to divide the real ship data sub units, and the small
sample navigation state determination set is obtained. The results of Kmeans clustering are compared with the determined set, and thenavigation state class is obtained by semantic classification. Factor
analysis is used to analyze the characteristics of navigation state data, so
as to reduce the dimension of data and make the classification result
better. Taking the inland navigation mark maintenance work boat data as
an example, this method is realized to label the ship navigation status forthe unknown large sample data set.

KEY WORDS: Real ship data; visual analysis; K-meansclustering; data dimensionality reduction.
INTRODUCTION
Ship's navigation state reflects the captain's thought of steering the ship.
In this paper, the captain's thought of steering is defined as the captain'sresponse to the operating requirements (such as constant speed cruise ordocking).By executing a series of actions (such as decelerating orsteering), the ship reaches a certain sailing state (such as straight sailingor berthing).Therefore, there is no doubt that the real ship data contains
rich ship behavior characteristic information such as the captain's
experience in operating the ship, the movement and maneuveringcharacteristics of the real ship, and etc. However, there are manydifficulties in collecting relevant information from the real ship data
through monitoring systems such as AIS. One problem is theunimprovable data quality of the actual ship’s data which is used in data
analyze. For data analyzing, data and methods are equally important andclosely related. When data quality is poor, no matter how good theanalysis method is, it might be unable to carry out accurate and reliable
results.Another important point is that these data is not connected to the3678