Vision-based Object Tracking in Marine Environments using Features from Neural Network Detections

Frederik Emil Thorsson Schöller, Mogens Blanke, Martin Krarup Plenge-Feidenhans'l, Lazaros Nalpantidis

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    Autonomous decision support is desired to enable navigation with a temporally unattended bridge or to have the vessel navigated remotely. In order to have safe navigation, it is crucial to correctly interpret the current situation given any scenario. Proper perception of the surrounding environment is essential for good situational awareness. This paper suggests a method for tracking objects that have been detected by a neural network. The method utilises features that have been computed during the detection step, thereby ensuring good features that are representative for the given objects while saving the time it would take to compute new features. The suggested method is evaluated on data acquired in Danish near-coastal waters. Evaluation shows that the tracking method is able to track the detections well with few switches of object identity. The method is shown to outperform a similar tracking algorithm, while keeping the speed needed for real-time applications.
    Original languageEnglish
    Book seriesIFAC-PapersOnLine
    Issue number2
    Pages (from-to)14517-14523
    Publication statusPublished - 2021
    EventIFAC World Congress 2020 - Virtual, Berlin, Germany
    Duration: 13 Jul 202017 Jul 2020


    ConferenceIFAC World Congress 2020
    Internet address


    • Autonomous Marine Vessels
    • Navigation
    • Computer Vision
    • Object Tracking


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