Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy

被引：17

作者：

Smith, James ^{[1
]}

Nouretdinov, Ilia ^{[1
]}

Craddock, Rachel ^{[2
]}

Offer, Charles ^{[2
]}

Gammerman, Alexander ^{[1
]}

机构：

[1] Royal Holloway Univ London, Comp Learning Res Ctr, Egham, Surrey, England

[2] Thales UK, London, England

来源：

STATISTICAL LEARNING AND DATA SCIENCES | 2015年 / 9047卷

基金：

英国工程与自然科学研究理事会;

关键词：

D O I：

10.1007/978-3-319-17091-6_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper investigates the problem of anomaly detection in the maritime trajectory surveillance domain. Conformal predictors in this paper are used as a basis for anomaly detection. A multi-class hierarchy framework is presented for different class representations. Experiments are conducted with data taken from shipping vessel trajectories using data obtained through AIS (Automatic Identification System) broadcasts and the results are discussed.

引用

页码：281 / 290

页数：10

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