Passive identification of vessel type through track motion analysis

Peter C. Yung; John M. Irvine

doi:10.1117/12.2561541

21 April 2020 Passive identification of vessel type through track motion analysis

Peter C. Yung, John M. Irvine

Proceedings Volume 11398, Geospatial Informatics X; 113980L (2020) https://doi.org/10.1117/12.2561541
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Maritime situational awareness depends on accurate knowledge of the locations, types, and activities of ocean-bound vessels. Such data can be gathered by analyzing the motion patterns of vessel tracks collected using coastal radar, visual identification, and Automatic Identification System (AIS) reports. We have developed a technique for predicting the types of vessels from abstract representations of their motion patterns. Our approach involves constructing multiple state sequences which represent activities syntactically. From these sequences, we generate multi-state transition matrices, which are the central feature used to train a support-vector machine classifier. Applying this technique to historical AIS data, our model successfully predicts vessel type even in cases where vessels do not follow known routes. Using only location information as the base feature for our model, we circumvent classification issues that arise from vessels' non-compliance with AIS regulations as well as the inability to visually identify vessels.

Conference Presentation

Citation Download Citation

Peter C. Yung and John M. Irvine "Passive identification of vessel type through track motion analysis", Proc. SPIE 11398, Geospatial Informatics X, 113980L (21 April 2020); https://doi.org/10.1117/12.2561541

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