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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Автор: Anneken, Mathias (auth)
Формат: Електронний ресурс Частина з книги
Опубліковано: KIT Scientific Publishing 2023
Серія:Karlsruher Schriften zur Anthropomatik
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Онлайн доступ:DOAB: download the publication
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Резюме: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.
Фізичний опис:1 electronic resource (264 p.)
ISBN:KSP/1000158519
Доступ:Open Access