Abstract
Anomaly detection of trajectory data is important and challenging in many real applications. Many anomalous trajectory detection algorithms have been developed, however most of them cannot well handle the complex trajectories with varying local densities. Additionally, trajectory similarity measure is usually difficult for complex trajectory data. In this paper, to address these two issues, we develop a novel anomalous trajectory detection technique with two important points. First we propose a new abnormal score, Global-Local Anomaly Score (GLAS), to sensitively quantify the abnormal degree of trajectories with varying local densities. Second an effective and fast measure, Extended Power enhanced Euclidean Distance (EPED), is designed to calculate the trajectory similarity. The proposed technique is evaluated on both synthetic and real-world trajectory data, showing that the new anomaly detector outperforms both classic and state-of-the-art methods.
This work has been funded by Natural Science Foundation of China under Grants No. 61471261 and No. 61771335. The author Yuejun Guo acknowledges support from Secretaria dUniversitats i Recerca del Departament dEmpresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)
Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)
Hawkins, D.M.: Identification of Outliers, vol. 11. Springer, Dordrecht (1980). https://doi.org/10.1007/978-94-015-3994-4
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15 (2009)
Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)
Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008)
Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014)
Lee, J.G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: Proceedings IEEE International Conference on Data Engineering, pp. 140–149 (2008)
Zhang, D., Li, N., Zhou, Z.H., Chen, C., Sun, L., Li, S.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings International Conference on Ubiquitous Computing, pp. 99–108 (2011)
Ge, Y., Xiong, H., Liu, C., Zhou, Z.H.: A taxi driving fraud detection system. In: Proceedings IEEE International Conference on Data Mining, pp. 181–190 (2011)
Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158–1173 (2014)
Kumar, D., Bezdek, J.C., Rajasegarar, S., Leckie, C., Palaniswami, M.: A visual-numeric approach to clustering and anomaly detection for trajectory data. Visual Comput. 33(3), 265–281 (2017)
Shirkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y.: A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS ONE 10(12), e0144059 (2015)
Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: Proceedings IEEE International Conference on Pattern Recognition, vol. 3, pp. 1135–1138 (2006)
Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010)
Guo, Y., Xu, Q., Luo, X., Wei, H., Bu, H., Sbert, M.: A group-based signal filtering approach for trajectory abstraction and restoration. Neural Comput. Appl. 12, 1–17 (2017)
Guo, Y., Xu, Q., Li, P., Sbert, M., Yang, Y.: Trajectory shape analysis and anomaly detection utilizing information theory tools. Entropy 19(7), 323 (2017)
Zhao, M., Saligrama, V.: Anomaly detection with score functions based on nearest neighbor graphs. In: Proceedings Advances in Neural Information Processing Systems, pp. 2250–2258 (2009)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)
Silva, D.F., De Souza, V.M. Batista, G.E.: Time series classification using compression distance of recurrence plots. In: Proceedings IEEE International Conference Data Mining, pp. 687–696 (2014)
Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_25
Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: Proceedings IEEE International Conference Data Mining, p. 8 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, C., Xu, Q., Peng, C., Guo, Y. (2019). Anomaly Detection Based on the Global-Local Anomaly Score for Trajectory Data. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-36802-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36801-2
Online ISBN: 978-3-030-36802-9
eBook Packages: Computer ScienceComputer Science (R0)