Multi-sensor systems for monitoring access to restricted areas: capabilities of the intrusion detection video analytical channel
B.N. Epifancev, A.A. Pyatkov, S.A. Kopeykin
1Siberian State Automobile-Road Academy, Omsk, Russia,
Full text of article: Russian language.
The analysis of research publications dealing with security of critical facilities and their protection from terrorist threats suggests that the multisensor systems proposed in the literature need to be supplemented with a video analytical system to enhance the reliability of detecting the intrusion to the restricted access area and recognition of intruders' intentions. An algorithm for solving the first part of the problem, based on the implementation of the principle of accumulation, is proposed. The issue of recognizing the intruders' intentions is partially addressed. The probabilities of an error of the first and second kind for the proposed intrusion detection algorithm are estimated.
restricted access area, intrusion detection, video analytical system, the principle of accumulation, detection rates.
Epifantsev BN, Pyatkov AA, Kopeykin SA. Multi-sensor systems for monitoring access to restricted areas: capabilities of the intrusion detection video analytical channel. Computer Optics 2016. 40(1). 121-129. DOI: 10.18287/2412-6179-2016-40-1-121-129.
- Borovsky AT, Borovsky AS, Tarasov AD. The general mathematical model of the system of physical protection of facilities. Herald of computer and information technologies. 2011; 10: 21-29.
- Epifantsev BN, Pyatkov AA. Mathematical model of the confrontation of the conflicting parties. Safety in Technosphere.2012; 5: 55-59.
- Degtyarev VA., Rodionov SL. Against the terrorist threat. Pipe-wire transport oil. 2010; 9: 20-52.
- Dubski R, Dubski R, Kastek M, Tezaskawka P, Pirtkowski T, Szustakowski M, Zyczkowski M. Concept of data processing in multi-sensor system for perimeter protection. Proceedings of the SPIE 2011; 8019: 8019OX.
- Wang J. FBG Intrusion Recognition Algorithm Based on SVM. Advanced Materials Research. 2012; 591–593: 1422-1427.
- Epifantsev BN. The acoustic method of diagnosing the state of underground pipelines: New Opportunities. Defectoscopy. 2014; 5: 9-13.
- Vishnyakov BV, Egorov AI, Malin IK. The statistical model of false recognition of objects in video surveillance systems. Herald of computer and information technologies 2013; 7: 42-46.
- Kononov VA, Konushin AS. Defining the types of objects in the video stream from the camera surveillance based on frame classification. Herald of computer and information technologies 2013; 10: 20-45.
- Kudinov IA, Pavlov OV, Holopov IS. Implementation of an algorithm for determining the spatial coordinates and the angular orientation of an object based on reference marks, using information from a single camera. Computer Optics 2015; 39(3): 413-419.
- Buch N, Velastin S. Local feature saliency classifier for real-time intrusion monitoring. Optical Engineering 2014; 53(7): 073108.
- Fishermen S.D. Video analytics – myths and real opportunities. Security algorithm 2010; 5: 150-153.
- Zwierzynski SS, Parfyonov IV. Detection and identification of offenders in optoelectronic surveillance systems. Radio engineering. 2010; 2: 63-67.
- Lipton AJ, Fujiyoshi H, Patil RS, Moving target classification and tracking from real-time video. Fourth IEEE Workshop on Applications of Computer Vi-sion’98: Proceedings 1998; 8-14.
- Haritaoglu L, Harwood D, Davis LS. W4: real-time surveillance of people and their activities. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2000; 22(8): 831-843.
- Viola PA. Detecting pedestrians using patterns of motion and appearance. Journal of Computer Vision 2005; 63(2): 153-161.
- Dollar P, Wojek C, Schiele B, Perona P. Pedestrian Detection: An Evaluation of the State of the Art. IEEE Trans. On Pattern Analysis and Machine Intelligence 2012; 34(4): 743-761.
- Continuous analysis of the video stream to the registration of the trajectory of moving objects in the camera view. Source: áhttp://www.synesis.ru/surveillance/products/Va-set/ñ.
- Pimenov AV. Great possibilities of video surveillance systems. Protection Technologies 2013; 2: 125-126.
- Luo Q, Kong X, Zeng G, Fan J. Human action detection via boosted local motion histograms. Machine Vision and Applications 2010; 21(3): 377-389.
- Escobar M, Kornprobst P. Action Recognition With a Bio-Inspired Freed-forward Motion Processing Model: The Richness of Center-Surround Interaction. European Conference on Computer Vision: Proceedings 2008: 186-199.
- Gorelick L, Blank M, Shechtman E, Irani M, Basri R. Actions as Space-Time Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 2007; 29(12): 2247-2253.
- Kiryati N, et al. Real Time Abnormal Motion Detection in Surveillance Video. 19th International Conference on Pattern Recognition 2008; 1-4.
- The i-LIDS User Guide. Imagery Library for Intelligent Detection Systems. United Kingdom: Home Office Centre for Applied Science and Technology; 2010: 63.
- Epifantsev BN, Lozhnikov PS, Sulavko AE. Comparison of algorithms for aggregation features in pattern recognition problems. Questions of information security 2012; 1: 60-66.
- Methods for constructing confidence intervals. Source: áhttp://edu.dvgups.ru/METDOC/ENF/VMATEM/WM/ME-TOD/MU_PZ/frame/2.htmñ.
- Meadows DH, Meadows DL, Randers J. Beyond the growt. Moscow: Progress, Pangaea; 1994.
- Ptitsyn NV. Built-in video analytics for detecting and tracking objects using multiscale features. Conference works «Graphicon 2010». 2010; 200-206.
© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: firstname.lastname@example.org; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20