Abstract
The paper deals with Surveillance Network Augmented by Retrieval (SUNAR) system – an information retrieval based wide area (video) surveillance system being developed as a free software at FIT, Brno University of Technology. It contains both standard and experimental techniques evaluated by NIST at the AVSS 2009 Multi-Camera Tracking Challenge and SUNAR performed comparably well.
In brief, SUNAR is composed of three basic modules – video processing, retrieval and the monitoring interface. Computer Vision Modules are based on the OpenCV Library for object tracking extended by feature extraction and network communication capability similar to MPEG-7. Information about objects and the area under surveillance is cleaned, integrated, indexed and stored in Video Retrieval Modules. They are based on the PostgreSQL database extended to be capable of similarity and spatio-temporal information retrieval, which is necessary for both non-overlapping surveillance camera system as well as information analysis and mining in a global context.
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References
BBC ’Talking’ CCTV scolds offenders. BBC News (April 4, 2007)
Brakatsoulas, S., Pfoser, D., Tryfona, N.: Modeling, Storing and Mining Moving Object Databases. In: IDEAS (2004)
Carmona, E.J., Martinez-Cantos, J., Mira, J.: A new video segmentation method of moving objects based on blob-level knowledge. Pattern Recognition Letters 29, 272–285 (2008)
CARETAKER Consortium. Caretaker Puts Knowledge to Good Use. Mobility, The European Public Transport Magazine 18(13) (2008)
Chmelar, P., Zendulka, J.: Visual Surveillance Metadata Management. Database and Expert Systems Applications. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 79–84. Springer, Heidelberg (2007)
Davenport, J.: Tens of thousands of CCTV cameras, yet 80% of crime unsolved. Evening Standard (September19, 2007)
Ellis, T., Black, J., Xu, M., Makris, D.: A Distributed Multi Camera Surveillance System. Ambient Intelligence, 107–138 (2005)
Bradski, G.R.: Learning OpenCV, p. 555. O’Reilly, Sebastopol (2008)
ISO/IEC JTC1/SC29/WG11. MPEG-7 Overview (2004)
Javed, O., Shah, M.: Automated Visual Surveillance: Theory and Practice, p. 110. Springer, Heidelberg (2008)
Mlich, J., Chmelar, P.: Trajectory classification based on Hidden Markov Models. In: Proceedings of 18th Int. Conf. on Computer Graphics and Vision, pp. 101–105 (2008)
Qu, W., Schonfeld, D., Mohamed, M.: Distributed Bayesian multiple-target tracking in crowded environments using multiple collaborative cameras. EURASIP J. Appl. Signal Process (1) (2007)
Qureshi, F.Z., Terzopoulos, D.: Multi-camera Control through Constraint Satisfaction for Persistent Surveillance. In: IEEE Conf. on Advanced Video and Signal Based Surveillance, pp. 211–218 (2008)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 3rd edn., p. 800. Thomson Engineering, Toronto (2007)
Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. Vision, Image and Signal Processing, IEE Proceedings 152(2), 192–204 (2005)
HOSDB. Home Office Multiple Camera Tracking Scenario data, scienceandresearch.homeoffice.gov.uk/hosdb/cctv-imaging-technology/video-based-detection-systems/i-lids [cit. 2009-11-17]
PETS: Performance Evaluation of Tracking and Surveillance, www.cvg.rdg.ac.uk/PETS2009 [cit. 2009-11]
Kasturi, R., et al.: Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(2), 319–336 (2009)
TRECVid Event Detection, www-nlpir.nist.gov/projects/tv2009/tv2009.html [cit. 2009-11-17]
Fiscus, J., Michel, M.: AVSS 2009 Multi-Camera Tracking Challenge, www.itl.nist.gov/iad/mig/tests/avss/2009/index.html [cit. 2009-11-17]
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Chmelar, P., Lanik, A., Mlich, J. (2010). SUNAR Surveillance Network Augmented by Retrieval. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_15
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DOI: https://doi.org/10.1007/978-3-642-17691-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17690-6
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