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Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction
Abstract : This paper describes conformal prediction techniques for detecting anomalous trajectories in the maritime domain. The data used in experiments were obtained from Automatic Identification System (AIS) broadcasts – a system for tracking vessel locations. A dimensionality reduction package is used and a kernel density estimation function as a non-conformity measure has been applied to detect anomalies. We propose average p-value as an efficiency criteria for conformal anomaly detection. A comparison with a k-nearest neighbours non-conformity measure is presented and the results are discussed.
Cited literature [9 references]
https://hal.inria.fr/hal-01391054
Contributor : Hal Ifip <>
Submitted on : Wednesday, November 2, 2016 - 5:18:32 PM
Last modification on : Thursday, March 5, 2020 - 5:41:12 PM
Long-term archiving on: : Friday, February 3, 2017 - 3:50:26 PM
Contributor : Hal Ifip <>
Submitted on : Wednesday, November 2, 2016 - 5:18:32 PM
Last modification on : Thursday, March 5, 2020 - 5:41:12 PM
Long-term archiving on: : Friday, February 3, 2017 - 3:50:26 PM
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