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Anomaliedetektion in räumlich-zeitlichen Datensätzen
Mathias Anneken
2023
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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.
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Keywords
- Anomaliedetektion
- anomaly detection
- Computer science
- Computing & information technology
- Machine learning
- maritime surveillance
- maritime Überwachung
- Maschinelles Lernen
- räumlich-zeitliche Daten
- Situation Analysis
- Situationsanalyse
- spatio-temporal data
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