Surveillance of Super-Extended Objects: Bimodal Approach
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Surveillance of Super-Extended Objects: Bimodal Approach

Authors: Andrey V. Timofeev, Dmitry Egorov

Abstract:

This paper describes an effective solution to the task of a remote monitoring of super-extended objects (oil and gas pipeline, railways, national frontier). The suggested solution is based on the principle of simultaneously monitoring of seismoacoustic and optical/infrared physical fields. The principle of simultaneous monitoring of those fields is not new but in contrast to the known solutions the suggested approach allows to control super-extended objects with very limited operational costs. So-called C-OTDR (Coherent Optical Time Domain Reflectometer) systems are used to monitor the seismoacoustic field. Far-CCTV systems are used to monitor the optical/infrared field. A simultaneous data processing provided by both systems allows effectively detecting and classifying target activities, which appear in the monitored objects vicinity. The results of practical usage had shown high effectiveness of the suggested approach.

Keywords: Bimodal processing, C-OTDR monitoring system, LPboost, SVM.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096023

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References:


[1] M. Elhoseiny, A. Bakry, and A. Elgammal, "MultiClass Object Classification in Video Surveillance Systems - Experimental Study”. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '13). IEEE Computer Society, Washington, DC, USA, 2013, pp. 788-793.
[2] D. G. Lowe, "Distinctive image features from scale-invariant keypoints”. IJCV, 60(2), 2014, pp.91–110.
[3] M. Tan, L. Wang, and I. W. Tsang, "Learning sparse svm for feature selection on very high dimensional datasets”, ICML, 2010, pp. 1047- 1054.
[4] A. Bosch, A. Zisserman, and X. Muoz, "Scene classification using a hybrid generative/discriminative approach,” IEEE Trans. Pattern Analysis and Machine Intell, 30(04), 2008, pp.712-727.
[5] A. E. Abdel-Hakim and A. A. Farag"CSIFT: A SIFT Descriptor with Color Invariant Characteristics,” Computer Vision and Image Processing Laboratory. (CVPR'06), 2006, pp.1978-1983.
[6] I. Laptev, "On space-time interest points”, IJCV, 64(2-3), 2005, pp.107– 123.
[7] H. Wang, A. Klaser, C. Schmid, and C.-L. Liu, "Dense trajectories and motion boundary descriptors for action recognition’, IJCV,103(1), 2013, pp.60–79.
[8] P. Merlmestein and S. Davis, "Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously. Spoken Sentences”, IEEE Trans. On ASSP, Aug, 1980. pp. 357-366.
[9] D. Titterington, A. Smith, U. Makov, Statistical Analysis of Finite Mixture Distributions. Wiley. ISBN 0-471-90763-4, 1985.
[10] M.A. Figueiredo, A.K. Jain, "Unsupervised Learning of Finite Mixture Models", IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (3), 2002, pp.381–396.
[11] S. Belongie, J. Malik, and J. Puzicha. "Matching shapes”, The 8th ICCV, Vancouver, Canada, pp. 454-461, 2001.
[12] Y. Ke and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors”, CVPR, Washington, DC, USA, 2004, pp. 66-75.
[13] N. Dalal, B. Triggs, "Histograms of Oriented Gradients for Human Detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, 2005, pp.886-893.
[14] V. Gehler, and Sebastian Nowozin, "On feature combination for multiclass object classification”, Peter. ICCV, IEEE, 2009, pp. 221-228.
[15] G. Ratsch, B. Scholkopf, A.J. Smola, S. Mika, T. Onoda, and K-R. Muller, "Robust ensemble learning”, In A.J. Smola, P. L. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, MIT Press, 1999, pр. 208–222.
[16] M.A. Hears, S.T. Dumais, E. Osman, J. Platt, and B. Scholkopf, "Support Vector Machines”, IEEE Intelligent Systems, vol. 13(4), 1998, pp.18-28.
[17] T. Jebara and R. Kondor, "Bhattacharyya and expected likelihood kernels”, In Proc.16th Annual Conference on Learning Theory (COLT 2003), 2003.
[18] M. Stone, "Asymptotics for and against cross-validation”, Biometrika, 1977, 64 (1), pp. 29–35.