NDLI: Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance

Content Provider	IEEE Xplore Digital Library
Author	Laxhammar, R. Falkman, G.
Copyright Year	2011
Description	Author affiliation: Informatics Research Centre, University of Skövde, Skövde, Sweden (Laxhammar, R.; Falkman, G.)
Abstract	Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with well-calibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameter-free dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.
Starting Page	1
Ending Page	8
File Size	484410
Page Count	8
File Format	PDF
ISBN	9781457702679
e-ISBN	9780982443828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Intl Society of Info Fusion
Subject Keyword	Trajectory Design automation Training Training data Detectors Shape Prediction algorithms automated surveillance Anomaly detection trajectory data Conformal prediction Hausdorff distance
Content Type	Text
Resource Type	Article

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