Surveillance Of Super-Extended Objects: Bimodal Approach

• Main Authors: Timofeev, Andrey V., Egorov, Dmitry
• Format: Text
• Language: English
• Published: Zenodo 2014
• Subjects: Bimodal processing; C-OTDR monitoring system; LPboost; SVM.; Canada; Dumais; Lowe; laptev
• Online Access: https://dx.doi.org/10.5281/zenodo.1096022; https://zenodo.org/record/1096022

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. : {"references": ["M. Elhoseiny, A. Bakry, and A. Elgammal, \"MultiClass Object\nClassification in Video Surveillance Systems - Experimental Study\".\nIn Proceedings of the 2013 IEEE Conference on Computer Vision and\nPattern Recognition Workshops (CVPRW '13). IEEE Computer Society,\nWashington, DC, USA, 2013, pp. 788-793.", "D. G. Lowe, \"Distinctive image features from scale-invariant keypoints\".\nIJCV, 60(2), 2014, pp.91\u2013110.", "M. Tan, L. Wang, and I. W. Tsang, \"Learning sparse svm for feature\nselection on very high dimensional datasets\", ICML, 2010, pp. 1047-\n1054.", "A. Bosch, A. Zisserman, and X. Muoz, \"Scene classification using a\nhybrid generative/discriminative approach,\" IEEE Trans. Pattern\nAnalysis and Machine Intell, 30(04), 2008, pp.712-727.", "A. E. Abdel-Hakim and A. A. Farag\"CSIFT: A SIFT Descriptor\nwith Color Invariant Characteristics,\" Computer Vision and Image\nProcessing Laboratory. (CVPR'06), 2006, pp.1978-1983.", "I. Laptev, \"On space-time interest points\", IJCV, 64(2-3), 2005, pp.107\u2013\n123.", "H. Wang, A. Klaser, C. Schmid, and C.-L. Liu, \"Dense trajectories and\nmotion boundary descriptors for action recognition', IJCV,103(1), 2013,\npp.60\u201379.", "P. Merlmestein and S. Davis, \"Comparison of Parametric\nRepresentations for Monosyllabic Word Recognition in Continuously.\nSpoken Sentences\", IEEE Trans. On ASSP, Aug, 1980. pp. 357-366.", "D. Titterington, A. Smith, U. Makov, Statistical Analysis of Finite\nMixture Distributions. Wiley. ISBN 0-471-90763-4, 1985.\n[10] M.A. Figueiredo, A.K. Jain, \"Unsupervised Learning of Finite Mixture\nModels\", IEEE Transactions on Pattern Analysis and Machine\nIntelligence 24 (3), 2002, pp.381\u2013396.\n[11] S. Belongie, J. Malik, and J. Puzicha. \"Matching shapes\", The 8th ICCV,\nVancouver, Canada, pp. 454-461, 2001.\n[12] Y. Ke and R. Sukthankar, \"PCA-SIFT: A more distinctive representation\nfor local image descriptors\", CVPR, Washington, DC, USA, 2004, pp.\n66-75.\n[13] N. Dalal, B. Triggs, \"Histograms of Oriented Gradients for Human\nDetection,\" IEEE Computer Society Conference on Computer Vision and\nPattern Recognition (CVPR'05), vol. 1, 2005, pp.886-893.\n[14] V. Gehler, and Sebastian Nowozin, \"On feature combination for\nmulticlass object classification\", Peter. ICCV, IEEE, 2009, pp. 221-228.\n[15] G. Ratsch, B. Scholkopf, A.J. Smola, S. Mika, T. Onoda, and K-R.\nMuller, \"Robust ensemble learning\", In A.J. Smola, P. L. Bartlett, B.\nScholkopf, and D. Schuurmans, editors, Advances in Large Margin\nClassifiers, MIT Press, 1999, p\u0440. 208\u2013222.\n[16] M.A. Hears, S.T. Dumais, E. Osman, J. Platt, and B. Scholkopf,\n\"Support Vector Machines\", IEEE Intelligent Systems, vol. 13(4), 1998,\npp.18-28.\n[17] T. Jebara and R. Kondor, \"Bhattacharyya and expected likelihood\nkernels\", In Proc.16th Annual Conference on Learning Theory (COLT\n2003), 2003.\n[18] M. Stone, \"Asymptotics for and against cross-validation\", Biometrika,\n1977, 64 (1), pp. 29\u201335."]}