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Open AccessArticle

SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection

Graphics and Imaging Lab, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain
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Sensors 2019, 19(1), 84; https://doi.org/10.3390/s19010084
Received: 14 November 2018 / Revised: 16 December 2018 / Accepted: 22 December 2018 / Published: 27 December 2018
To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD +. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of predefining it without any prior knowledge, which makes the algorithm more usable in practical applications. We present a modified Hausdorff distance measure that takes into account the direction difference and also reduces the computational complexity. In addition, the anomaly detection is more flexible and accurate via a re-do strategy. Extensive experiments on both real-world and synthetic data show that SHNN-CAD + outperforms SHNN-CAD with regard to accuracy and running time. View Full-Text
Keywords: online anomaly detection; trajectory data; adaptive anomaly threshold; Hausdorff distance with constraint window online anomaly detection; trajectory data; adaptive anomaly threshold; Hausdorff distance with constraint window
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Figure 1

  • Externally hosted supplementary file 1
    Link: http://gilabparc.udg.edu/trajectory_data/SyntheticTrajectories.zip
    Description: Created trajectory data for experiments in Section 4.3 website of code.txt provides of the link of MATLAB implementation of the proposed algorithm. sensors-398290_highlight.pdf provides the changes between the original manuscript and the revised version.
MDPI and ACS Style

Guo, Y.; Bardera, A. SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection. Sensors 2019, 19, 84.

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