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