Tracking multiple moving objects in real-time in a dynamic threat environment is an important element in national security and surveillance system. It helps pinpoint and distinguish potential candidates posing threats from other normal objects and monitor the anomalous trajectories until intervention. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm that can associate the sequential observations of locations and motion with the underlying moving objects, and therefore, build the trajectories of the objects as the objects are moving. In this work, we develop a spatio-temporal approach for tracking maritime vessels as the vessel’s location and motion observations are collected by an Automatic Identification System. The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment. Three training datasets and five test sets are provided in the challenge and a set of quantitative performance metrics is devised by the data challenge organizer for evaluating and comparing resulting methods developed by participants. When our proposed track association algorithm is applied to the five test sets, the algorithm scores a very competitive performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data


    Contributors:
    Ahmed, Imtiaz (author) / Jun, Mikyoung (author) / Ding, Yu (author)

    Published in:

    Publication date :

    2022-11-01


    Size :

    2505568 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    SPATIO-TEMPORAL TRACK DENSITY SHAPING

    FREWEN THOMAS / MOSTAFA HALA / DUBOIS MICHAEL D et al. | European Patent Office | 2023

    Free access

    LOST TRACK DATA ESTIMATION SYSTEM OF MARINE VESSEL USING MACHINE LEARNING

    OH JAE YONG / KIM HYE JIN / PARK SEKIL | European Patent Office | 2019

    Free access

    Ship route mark discrimination method and system based on track spatio-temporal characteristics

    LIN DEYIN / ZHANG SHENGZHENG / TAO PENGYU et al. | European Patent Office | 2024

    Free access