The authors present an object detection system and its results for finding ships in maritime video for the purpose of ship traffic management. They compare the process of training the detection system for both offline and online learning. The system is learned in an online fashion where training samples are used in an iterative cycle and discarded after each video frame. The authors show that this online learning gives a performance that approaches the offline learning case and in certain parts even outperforms the offline detector. The online training has considerable practical advantages, such as much smaller memory requirements, the inherent adaptivity to changing objects and an iterative extensibility. However, due to the adaptivity, the performance of the online system can locally degrade. An interesting feature is that only a few images are sufficient to already obtain reasonable detection performance.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online learning for ship detection in maritime surveillance


    Contributors:


    Publication date :

    2010


    Size :

    8 Seiten, 7 Bilder, 1 Tabelle, 5 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    An Automated SAR-based Method for Ship Detection in Maritime Surveillance System

    Xu, Zidong / Zhang, Di / Fan, Liang et al. | IEEE | 2023


    Maritime surveillance system

    KANG YOUNG SHIN / KOO SAM OK / PARK BUM JIN et al. | European Patent Office | 2017

    Free access

    MARITIME SURVEILLANCE RADAR

    MARTINEZ JOSE MARQUEZ | European Patent Office | 2024

    Free access

    MARITIME SURVEILLANCE SYSTEM

    KANG YOUNG SHIN / KOO SAM OK / PARK BUM JIN et al. | European Patent Office | 2017

    Free access

    MARITIME SURVEILLANCE RADAR

    MARTINEZ JOSE MARQUEZ | European Patent Office | 2024

    Free access