An Automated Surveillance System Based on Multi-Processor and GPU Architecture
Abstract
Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelerate the system and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section illustrates the results with comparison of some of the most recent approaches.
Keywords:
Surveillance system, suspicious behaviors, multi-processor, GPUDownloads
References
E. R. Davies, Computer and Machine Vision, Academic Press, 2012
R. Arroyo, J. J. Yebes, L. M. Bergasa, I. G. Daza, J. Almazan, “Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls”, Expert systems with Applications, Vol. 42, No. 21, pp 7991-8005, 2015
J. K. Aggarwal, M. S. Ryoo, “Human activity analysis: a review”, ACM computing surveys, Vol. 43, No. 16, 2011 DOI: https://doi.org/10.1145/1922649.1922653
R. Shimonski, “Digital reconnaissance and surveillance”, Chapter 1 in Cyber Reconnaissance, Surveillance and Defense, Elsevier, 2015 DOI: https://doi.org/10.1016/B978-0-12-801308-3.00001-9
L. Deligiannidis, H. R. Arabnia, Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, Morgan Kauffman 2015
Electronic Frontier Foundation, “NSA Spying”, Available at https://www.eff.org/nsa-spying
T. D. Raty, “Survey on contemporary remote surveillance systems for public safety”, IEEE Transactions on Systems, Man and Cybernetics Part C, Vol. 40, No. 5, pp. 493–515, 2010 DOI: https://doi.org/10.1109/TSMCC.2010.2042446
L. D. Stefano, C. S. Regazzoni, D. Schonfeld, “Advanced video-based surveillance”, EURASIP Journal on Image and Video Processing (JIVP), Vol. 2001, No. 1, p. 857084, 2011 DOI: https://doi.org/10.1155/2011/857084
S. Brutzer, B. Hoferlin, G. Heidemann, “Evaluation of background subtraction techniques for video surveillance”, IEEE Conference on Computer Vision and Pattern Eecognition, pp. 1937–1944, 2011 DOI: https://doi.org/10.1109/CVPR.2011.5995508
L. Maddalena, A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications”, IEEE Transactions on Image Processing, Vol 17, No. 7, pp. 1168–1177, 2008 DOI: https://doi.org/10.1109/TIP.2008.924285
J. M. Odobez, J. Yao, “Multi-layer background subtraction based on color and texture”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007
F. E. Baf, T. Bouwmans, B. Vachon, “Background modeling using mixture of gaussians for foreground detection - a survey”, Recent Patents on Computer Science, Vol. 1, No.3, pp. 219–237, 2008 DOI: https://doi.org/10.2174/2213275910801030219
R. Arroyo, J. J. Yebes, L. M. Bergasa, G. Daza, J. Almazn, “Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls”, Expert Systems with Applications, Vol. 42, No. 21, pp. 7991–8005, 2015 DOI: https://doi.org/10.1016/j.eswa.2015.06.016
D. Chau, M. Thonnat, F. Bremond, E. Corvee, “Online parameter tuning for object tracking algorithms”, Image and Vision Computing, Vol 32, No. 4, pp. 287–302, 2014 DOI: https://doi.org/10.1016/j.imavis.2014.02.003
S. Alvarez, D. Llorca, M. Sotelo, “Hierarchical camera auto-calibration for traffic surveillance systems”, Expert Systems With Applications , Vol. 41, No. 4, pp. 1532–1542, 2014 DOI: https://doi.org/10.1016/j.eswa.2013.08.050
Z. L. Szpak, J. R. Tapamo, “Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set”, Expert Systems With Applications, Vol. 38, No. 6, pp. 6669–6680, 2011 DOI: https://doi.org/10.1016/j.eswa.2010.11.068
M. Cristani, R. Raghavendra, A. Del Bue, V. Murino, “Human behavior analysis in video surveillance: A social signal processing perspective”, Neurocomputing, Vol. 100, pp. 86–97, 2012 DOI: https://doi.org/10.1016/j.neucom.2011.12.038
W. Hu, T. Tan, L. Wang, S. Maybank, “A survey on visual surveillance of object motion and behaviors”, IEEE Transactions on Systems, Man and Cybernetics Part C, Vol 34, No. 3, pp. 334–352, 2004 DOI: https://doi.org/10.1109/TSMCC.2004.829274
C. Rao, M. Shah, “View-invariance in action recognition”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. II-316-II-322, 2001
S. Savarese, A. Delpozo, J. Niebles, L. Fei-Fei, “Spatial-temporal correlations for unsupervised action classification”, IEEE Workshop on Motion and Video Computing, pp. 1-8, 2008 DOI: https://doi.org/10.1109/WMVC.2008.4544068
M. D. Rodriguez, J. Ahmed, M. Shah, “Action MACH: a spatiotemporal maximum average correlation height filter for action recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008 DOI: https://doi.org/10.1109/CVPR.2008.4587727
M. S. Ryoo, J. K Aggarwal, “Spatio-temporal relationship match: video structure comparison for recognition of complex human activities”, IEEE 12th International Conference on Computer Vision, pp. 1593-1600, 2009 DOI: https://doi.org/10.1109/ICCV.2009.5459361
H. Jiang, M. Drew, Z. Li, “Successive convex matching for action detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1646-1653, 2006
A. Veeraraghavan, R. Chellappa, A. Roy-Chowdhury, “The function space of an activity”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 959-968, 2006.
P. Natarajan, R. Nevatia, “Coupled hidden semi-markov models for activity recognition”, IEEE Workshop on Motion and Video Computing, pp. 10, 2007 DOI: https://doi.org/10.1109/WMVC.2007.12
D. Damen, D. Hogg, “Recognizing linked events: searching the space of feasible explanations”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 927-934, 2009 DOI: https://doi.org/10.1109/CVPR.2009.5206636
S. W. Joo, R. Chellappa, “Attribute grammar-based event recognition and anomaly detection”, IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 107, 2006
M. S. Ryoo, J. K. Aggarwal, “Semantic representation and recognition of continued and recursive human activities”, International Journal of Computer Vision, Vol. 82, No. 1, pp. 1-24, 2009 DOI: https://doi.org/10.1007/s11263-008-0181-1
M. S. Ryoo, J. K. Aggarwal, “Recognition of composite human activities through context-free grammar based representation”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1709-1718, 2006
C. Mu, J. Xie, W. Yan, T. Liu, “A fast recognition algorithm for suspicious behavior in high definition videos”, Multimedia Systems, Vol. 22, No. 3, pp 275-285, 2016 DOI: https://doi.org/10.1007/s00530-015-0456-7
S. Shaikh, A. Maiti, N. Chaki, “A new image binarization method using iterative partitioning”, Machine Vision and Applications, Vol. 24, No. 2, pp 337-350, 2013 DOI: https://doi.org/10.1007/s00138-011-0402-4
J. Wang, L. Wu, Y. Liu, “Nios II Processor-Based Fingerprint Identification System”, Nios II Embedded Processor Design Contest—Outstanding Designs 2007
A. Beristain, M. Grana, “A stable skeletonization for tabletop gesture recognition”, International Conference on Computational Science and Its Applications, pp. 610-621, 2010 DOI: https://doi.org/10.1007/978-3-642-12156-2_46
M. Ben Ayed, F. Bouchhima, M. Abid, “A novel application of the classifier DECOC based on fingerprint identification”, Workshop on Database and Expert Systems Applications, pp. 288-292, 2010 DOI: https://doi.org/10.1109/DEXA.2010.64
C. Schuldt, I. Laptev, B. Caputo, “Recognizing human actions: a local SVM approach”, 17th International Conference on Pattern Recognition, Vol. 3, pp. 32-36, 2004 DOI: https://doi.org/10.1109/ICPR.2004.1334462
M. Marszalek, I. Laptev, C. Schmid, “Actions in context”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 2929-2936, 2009 DOI: https://doi.org/10.1109/CVPR.2009.5206557
K. Schindler, L. J. Van Gool, “Action snippets: how many frames does human action recognition require?”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008 DOI: https://doi.org/10.1109/CVPR.2008.4587730
A. Gilbert, J. Illingworth, R. Bowden, “Fast realistic multi-action recognition using mined dense spatio-temporal features”, IEEE 12th International Conference on Computer Vision, pp. 925-931, 2009 DOI: https://doi.org/10.1109/ICCV.2009.5459335
A. Yao, J. Gall, L. Van Gool, “A hough transform-based voting framework for action recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 2061-2068, 2010 DOI: https://doi.org/10.1109/CVPR.2010.5539883
S. Sadanand, J. J. Corso, “Action Bank: a high-level representation of activity in video”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1234-1241, 2012 DOI: https://doi.org/10.1109/CVPR.2012.6247806
T. N. Hau, W. I. Robert, N. R. Ryan, P. B. Randy, “Real-time video surveillance on an embedded, programmable platform”, Microprocessors and Microsystems, Vol. 37,No. 6-7, pp. 562–571, 2013 DOI: https://doi.org/10.1016/j.micpro.2013.06.003
M. Casares, S. Velipasalar, “Resource-efficient salient foreground detection for embedded smart cameras by tracking feedback”, IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 369–375, 2010 DOI: https://doi.org/10.1109/AVSS.2010.50
J. Schlessman, C. Y. Chen, B. Ozer, K. Fujino, K. Itoh, W. Wolf, “Hardware/software co-design of an FPGA-based embedded tracking system”, Conference on Computer Vision and Pattern Recognition Workshop, pp. 123–130, 2006
H. Meng, C. Freeman, N. Pears, C. Bailey, “Real-time human action recognition on an embedded, reconfigurable video processing architecture”, Journal of Real-Time Image Processing, Vol. 3, No. 3, pp.163–176, 2008 DOI: https://doi.org/10.1007/s11554-008-0073-1
[45] B.M.A Amer, and S.A.R Al-Attas, “Smart surveillance using PDA, Word Academy of Science”, Engineering and Technology, Vol. 66, pp.251–255, 2010
S. Saadi, M. Touiza, F. Kharfi, A. Guessoum, “Dyadic wavelet for image coding implementation on a Xilinx MicroBlaze processor: Application to neutron radiography”, Applied Radiation and Isotopes, Vol. 82, pp.200-210, 2013 DOI: https://doi.org/10.1016/j.apradiso.2013.08.001
D. Watson, A. Ahmadinia, “Memory customisations for image processing applications targeting MPSoCs”, Integration, the VLSI Journal, Vol. 51, pp. 72-80, 2015 DOI: https://doi.org/10.1016/j.vlsi.2015.06.004
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
