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...
保存先:
| 第一著者: | |
|---|---|
| フォーマット: | Online |
| 言語: | ドイツ語 |
| 出版事項: |
KIT Scientific Publishing
2023
|
| 主題: | |
| オンライン･アクセス: | OCN: 1403109722 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | 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. |
|---|