Learning and Classification of Car Trajectories in Road Video by String Kernels

Luc Brun, Alessia Saggese, Mario Vento

2013

Abstract

An abnormal behavior of a moving vehicle or a moving person is characterized by an unusual or not expected trajectory. The definition of expected trajectories refers to supervised learning, where an human operator should define expected behaviors. Conversely, definition of usual trajectories, requires to learn automatically the dynamic of a scene in order to extract its typical trajectories. We propose, in this paper, a method able to identify abnormal behaviors based on a new unsupervised learning algorithm. The original contributions of the paper lies in the following aspects: first, the evaluation of similarities between trajectories is based on string kernels. Such kernels allow us to define a kernel-based clustering algorithm in order to obtain groups of similar trajectories. Finally, identification of abnormal trajectories is performed according to the typical trajectories characterized during the clustering step. The experimentation, conducted over a real dataset, confirms the efficiency of the proposed method.

References

  1. Acampora, G., Foggia, P., Saggese, A., and Vento, M. (2012). Combining neural networks and fuzzy systems for human behavior understanding. In Proceedings of the IEEE AVSS Conference, pages 88-93.
  2. Aggarwal, J. and Ryoo, M. (2011). Human activity analysis: A review. ACM Comput. Surv., 43(3):16:1-16:43.
  3. Brun, L., Saggese, A., and Vento, M. (2012). A clustering algorithm of trajectories for behaviour understanding based on string kernels. In Proceedings of the 2012 SITIS Conference, pages 267-274. IEEE.
  4. Brun, L. and Trémeau, A. (2002). Digital Color Imaging Handbook, chapter 9 : Color quantization, pages 589- 637. Electrical and Applied Signal Processing. CRC Press.
  5. Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Comput. Surv., 41(3):15:1-15:58.
  6. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20:273-297.
  7. Cuturi, M. (2011). Fast global alignment kernels. In Getoor, L. and Scheffer, T., editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML 7811, pages 929-936, New York, NY, USA. ACM.
  8. d'Acierno, A., Leone, M., Saggese, A., and Vento, M. (2012a). An efficient strategy for spatio-temporal data indexing and retrieval. In Proceedings of the KDIR Conference, pages 227,232.
  9. d'Acierno, A., Leone, M., Saggese, A., and Vento, M. (2012b). A system for storing and retrieving huge amount of trajectory data, allowing spatio-temporal dynamic queries. In Proceedings of the IEEE ITS Conference, pages 989,994.
  10. Di Lascio, R., Foggia, P., Saggese, A., and Vento, M. (2012). Tracking interacting objects in complex situations by using contextual reasoning. In Csurka, G. and Braz, J., editors, VISAPP (2), pages 104-113. SciTePress.
  11. Foggia, P., Percannella, G., Sansone, C., and Vento, M. (2008). A graph-based algorithm for cluster detection. IJPRAI, 22(5):843-860.
  12. Morris, B. and Trivedi, M. (2011). Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(11):2287 -2301.
  13. Neuhaus, M. and Bunke, H. (2006). Edit distance-based kernel functions for structural pattern classification. Pattern Recognition, 39(10):1852 - 1863.
  14. Saigo, H., Vert, J.-P., Ueda, N., and Akutsu, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics, 20(11):1682-1689.
  15. Schaeffer, S. (2007). Graph clustering. Computer Science Review, 1(1):27-64.
  16. Schölkopf, B., Smola, A., and Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10(5):1299-1319.
  17. Wang, X., Ma, K. T., Ng, G.-W., and Grimson, W. E. (2011). Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. Int. J. Comput. Vision, 95:287-312.
  18. Zhou, Y., Yan, S., and Huang, T. (2007). Detecting anomaly in videos from trajectory similarity analysis. In Multimedia and Expo, 2007 IEEE International Conference on, pages 1087 -1090.
Download


Paper Citation


in Harvard Style

Brun L., Saggese A. and Vento M. (2013). Learning and Classification of Car Trajectories in Road Video by String Kernels . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 709-714. DOI: 10.5220/0004301207090714


in Bibtex Style

@conference{visapp13,
author={Luc Brun and Alessia Saggese and Mario Vento},
title={Learning and Classification of Car Trajectories in Road Video by String Kernels},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={709-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004301207090714},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Learning and Classification of Car Trajectories in Road Video by String Kernels
SN - 978-989-8565-47-1
AU - Brun L.
AU - Saggese A.
AU - Vento M.
PY - 2013
SP - 709
EP - 714
DO - 10.5220/0004301207090714