Anomaliedetektion in räumlich-zeitlichen Datensätzen
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...
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| Main Author: | |
|---|---|
| Format: | Electronic Book Chapter |
| Published: |
KIT Scientific Publishing
2023
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| Series: | Karlsruher Schriften zur Anthropomatik
51 |
| Subjects: | |
| Online Access: | OAPEN Library: download the publication OAPEN Library: description of the publication |
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| 520 | |a 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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| 546 | |a German | ||
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| 653 | |a spatio-temporal data; situation analysis; anomaly detection; räumlich-zeitliche Daten; Maritime Überwachung; Anomaliedetektion; maritime surveillance; Situationsanalyse; machine learning; Maschinelles Lernen | ||
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