Deep Dissimilarity Measure for Trajectory Analysis

  • Reza Arfa
  • Rubiyah YusofEmail author
  • Parvaneh Shabanzadeh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)


Quantifying dissimilarities between two trajectories is a challenging yet fundamental task in many trajectory analysis systems. Existing methods are computationally expensive to calculate. We proposed a dissimilarity measure estimate for trajectory data by using deep learning methodology. One advantage of the proposed method is that it can get executed on GPU, which can significantly reduce the execution time for processing large number of data. The proposed network is trained using synthetic data. A simulator to generate synthetic trajectories is proposed. We used a publicly available dataset to evaluate the proposed method for the task of trajectory clustering. Our experiments show the performance of our proposed method is comparable with other well-known dissimilarity measures while it is substantially faster to compute.


Trajectory analysis Dissimilarity measure Deep learning LSTM 


  1. 1.
    Teimouri, M., Indahl, U., Sickel, H., Tveite, H.: Deriving animal movement behaviors using movement parameters extracted from location data. ISPRS Int. J. Geo-Inf. 7(2), 78 (2018)CrossRefGoogle Scholar
  2. 2.
    Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Weiming, H., Xi, L., Guodong, T., Maybank, S., Zhongfei, Z.: An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1051–1065 (2013)CrossRefGoogle Scholar
  5. 5.
    Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)Google Scholar
  6. 6.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: 2002 Proceedings of 18th International Conference on Data Engineering, pp. 673–684. IEEE (2002)Google Scholar
  7. 7.
    Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. Presented at the Proceedings of the 2005 ACM SIGMOD International Conference on Management of data, Baltimore, Maryland (2005)Google Scholar
  8. 8.
    Wang, X., Ma, K.T., Ng, G.W., Grimson, W.E.: Trajectory analysis and semantic region modeling using a nonparametric Bayesian model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  9. 9.
    Piciarelli, C., Foresti, G.L.: On-line trajectory clustering for anomalous events detection. Pattern Recognit. Lett. 27(15), 1835–1842 (2006)CrossRefGoogle Scholar
  10. 10.
    Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. Presented at the Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, vol. 30 (2004)Google Scholar
  11. 11.
    Atev, S., Masoud, O., Papanikolopoulos, N.: Learning traffic patterns at intersections by spectral clustering of motion trajectories. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4851–4856. IEEE (2006)Google Scholar
  12. 12.
    Alt, H.: The computational geometry of comparing shapes. In: Albers, S., Alt, H., Näher, S. (eds.) Efficient Algorithms. LNCS, vol. 5760, pp. 235–248. Springer, Heidelberg (2009). Scholar
  13. 13.
    Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158–1173 (2014)CrossRefGoogle Scholar
  14. 14.
    Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: experimental studies and comparative evaluation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 312–319 (2009)Google Scholar
  15. 15.
    Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: 2006 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1135–1138 (2006)Google Scholar
  16. 16.
    Buza, K., Nanopoulos, A., Schmidt-Thieme, L.: Fusion of similarity measures for time series classification. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011. LNCS (LNAI), vol. 6679, pp. 253–261. Springer, Heidelberg (2011). Scholar
  17. 17.
    Weiming, H., Xuejuan, X., Zhouyu, F., Xie, D., Tieniu, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1450–1464 (2006)CrossRefGoogle Scholar
  18. 18.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  19. 19.
    Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)Google Scholar
  20. 20.
    Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. Trans. Neural Netw. 5(2), 157–166 (1994)CrossRefGoogle Scholar
  21. 21.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  22. 22.
    Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)Google Scholar
  23. 23.
    Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3547–3555 (2015)Google Scholar
  24. 24.
    Ordóñez, F.J., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)CrossRefGoogle Scholar
  25. 25.
    Zimmermann, H.-G., Tietz, C., Grothmann, R.: Forecasting with recurrent neural networks: 12 tricks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 687–707. Springer, Heidelberg (2012). Scholar
  26. 26.
    Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retr. 12(4), 461–486 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Reza Arfa
    • 1
  • Rubiyah Yusof
    • 1
    Email author
  • Parvaneh Shabanzadeh
    • 1
  1. 1.Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT)Universiti Teknologi MalaysiaKuala LumpurMalaysia

Personalised recommendations