Novosibirsk State University Journal of Information Technologies
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All Issues >> Contents: Volume 15, Issue No 3 (2017)

Algorithms for Detecting Incident for Automated Video Surveillance Systems
M. S. Gordin, S. A. Ivanov

Novosibirsk State University

DOI: DOI 10.25205/1818-7900-2017-15-3-21-30
UDC code: 004.932

Abstract
The article is devoted to real-time algorithms for detecting events described by four scenarios: movement in the prohibited direction, movement in the sterile zone, abandonment (abduction) of the object, throwing the object. The main idea of algorithms is the analysis of the trajectories of moving objects, for obtaining which two different approaches are proposed in the article.

Key Words
digital image processing, video analytics, video surveillance systems, object detection, tracking

How to cite:
Gordin M. S., Ivanov S. A. Algorithms for Detecting Incident for Automated Video Surveillance Systems // Vestnik NSU Series: Information Technologies. - 2017. - Volume 15, Issue No 3. - P. 21–30. - DOI 10.25205/1818-7900-2017-15-3-21-30. - ISSN 1818-7900. (in Russian).

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References
1. Ainsworth T. Buyer Beware // Security Oz. 2002. Vol. 19. P. 18–26.
2. Singla M. Motion Detection Based on Frame Difference Method International // Journal of Information & Computation Technology. 2014. Vol. 4. No. 15. P. 1559–1565.
3. Zivkovic Z. Improved adaptive gaussian mixture model for background subtraction // IEEE Int. Conf. Pattern Recognition. 2004. Vol. 2. P. 28–31
4. Bouwmans T., El Baf F., Vachon B. Background Modeling using Mixture of Gaussians for Foreground Detection – A Survey // Recent Patents on Computer Science. 2008. Vol. 1. P. 219–237.
5. Dalal N., Triggs B. Histograms of Oriented Gradients for Human Detection // IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005. P. 886–893.
6. Felzenszwalb P., Girshick R., McAllester D., Ramanan D. Object Detection with Discriminatively Trained Part Based Models // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. Vol. 32. No. 9. P. 1627–1645.
7. Girshick R., Donahue J., Darrell T., Malik J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation // IEEE Conference on Computer Vision and Pattern Recognition. 2014. P. 580–587.
8. Je C., Park H. M. Optimized Hierarchical Block Matching for Fast and Accurate Image Registration // Signal Processing: Image Communication. 2013. Vol. 28. No. 7. P. 779–791.
9. Aslani S., Mahdavi-Nasab H. Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance // International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering. 2013. Vol. 7. No. 9. P. 1252–1256.
10. Zaveri M. A., Merchant S. N., Desai U. B. Small and Fast Moving Object Detection and Tracking in Sports Video Sequences // IEEE International Conference on Multimedia and Expo. 2004. Vol. 3. P. 1539–1542.
11. Comaniciu D., Ramesh V., Meer P. Kernel-based object tracking // IEEE Transactions on pattern analysis and machine intelligence. 2003. Vol. 25. No. 5. P. 564–577.
12. Chitaliya N. G., Trivedi A. I. Novel block matching algorithm using predictive motion vector for video object tracking based on color histogram // 3rd International Conference on Electronics Computer Technology. 2011. Vol. 5. P. 81–85.
13. Hingane P., Shirsat S. Object Tracking Using Joint Color-Texture Histogram // International Journal of Science and Research. 2013. P. 2603–2606
14. Tissainayagama P., Suterb D. Object tracking in image sequences using point features // Pattern Recognition. 2005. Vol. 38. No. 1. P. 105–113.
15. Fischler M. A., Bolles R. C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography // Communications of the ACM 24. 1981. P. 381–395.

Publication information
Main title Vestnik NSU Series: Information Technologies, Volume 15, Issue No 3 (2017).
Parallel title: Novosibirsk State University Journal of Information Technologies Volume 15, Issue No 3 (2017).

Key title: Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriâ: Informacionnye tehnologii
Abbreviated key title: Vestn. Novosib. Gos. Univ., Ser.: Inf. Tehnol.
Variant title: Vestnik NGU. Seriâ: Informacionnye tehnologii

Year of Publication: 2017
ISSN: 1818-7900 (Print), ISSN 2410-0420 (Online)
Publisher: Novosibirsk State University Press
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