Human support in surveillance tasks is crucial due to the overwhelming amount of sensor data. This work focuses on the development of data fusion methods using the maritime domain as an example. Various anomalies are investigated, evaluated using real vessel traffic data and tested with experts. For this purpose, situations of interest and anomalies are modelled and evaluated based on different machine learning methods
Anomaliedetektion in räumlich-zeitlichen Datensätzen
2023
1 Online-Ressource (264 p.)
Buch
Elektronische Ressource
Unbekannt
Anomaliedetektion in räumlich-zeitlichen Datensätzen
GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2023
|Verfahren zum Verbreiten von räumlich-zeitlichen Verkehrsinformationen
Europäisches Patentamt | 2021
|