Fast Agglomerative Information Bottleneck Based Trajectory Clustering

  • Yuejun Guo
  • Qing Xu
  • Yang Fan
  • Sheng Liang
  • Mateu Sbert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

Clustering is an important data mining technique for trajectory analysis. The agglomerative Information Bottleneck (aIB) principle is efficient for obtaining an optimal number of clusters without the direct use of a trajectory distance measure. In this paper, we propose a novel approach to trajectory clustering, fast agglomerative Information Bottleneck (faIB), to speed up aIB by two strategies. The first strategy is to do “clipping” based on the so-called feature space, calculating information losses only on fewer cluster pairs. The second is to select and merge more candidate clusters, reducing iterations of clustering. Remarkably, faIB considerably runs above 10 times faster than aIB achieving almost the same clustering performance. In addition, extensive experiments on both synthetic and real datasets demonstrate that faIB performs better than the clustering approaches widely used in practice.

Keywords

aIB faIB Trajectory clustering Speedup 

References

  1. 1.
    Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2013)MATHGoogle Scholar
  2. 2.
    Anjum, N., Cavallaro, A.: Multifeature object trajectory clustering for video analysis. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1555–1564 (2008)CrossRefGoogle Scholar
  3. 3.
    Calderara, S., Prati, A., Cucchiara, R.: Mixtures of von mises distributions for people trajectory shape analysis. IEEE Trans. Circ. Syst. Video Technol. 21(4), 457–471 (2011)CrossRefGoogle Scholar
  4. 4.
    Goldberger, J., Gordon, S., Greenspan, H.: Unsupervised image-set clustering using an information theoretic framework. IEEE Trans. Image Process. 15(2), 449–458 (2006)CrossRefGoogle Scholar
  5. 5.
    Guo, Y., Xu, Q., Yang, Y., Liang, S., Liu, Y., Sbert, M.: Anomaly detection based on trajectory analysis using kernel density estimation and information bottleneck techniques. Technical report 108, University of Girona (2014)Google Scholar
  6. 6.
    Annoni Jr., R.A., Forster, C.H.Q.: Analysis of aircraft trajectories using fourier descriptors and kernel density estimation. In: Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, pp. 1441–1446 (2012)Google Scholar
  7. 7.
    Junejo, I.N., Javed, O., Shah, M.: Multi feature path modeling for video surveillance. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 716–719. IEEE (2004)Google Scholar
  8. 8.
    Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158–1173 (2014)CrossRefMATHGoogle Scholar
  9. 9.
    Majecka, B.: Statistical models of pedestrian behaviour in the forum. Master’s thesis, School of Informatics, University of Edinburgh (2009)Google Scholar
  10. 10.
    Mitsch, S., Müller, A., Retschitzegger, W., Salfinger, A., Schwinger, W.: A survey on clustering techniques for situation awareness. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 815–826. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: experimental studies and comparative evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 312–319 (2009)Google Scholar
  12. 12.
    Morris, B.T., Trivedi, M.M.: Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2287–2301 (2011)CrossRefGoogle Scholar
  13. 13.
    Morris, B.T., Trivedi, M.M.: Understanding vehicular traffic behavior from video: a survey of unsupervised approaches. J. Electron. Imaging 22(4), 041113–041113 (2013)CrossRefGoogle Scholar
  14. 14.
    Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1544–1554 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yuejun Guo
    • 1
    • 2
  • Qing Xu
    • 1
  • Yang Fan
    • 1
  • Sheng Liang
    • 1
  • Mateu Sbert
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Graphics and Imaging LaboratoryUniversitat de GironaGironaSpain

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