Motion Detection of Some Geometric Shapes in Video Surveillance

Larbi Guezouli; Hanane Belhani

doi:doi:10.11648/j.ajdmkd.20170201.12

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Motion Detection of Some Geometric Shapes in Video Surveillance

Larbi Guezouli, Hanane Belhani

Published in American Journal of Data Mining and Knowledge Discovery (Volume 2, Issue 1)

Received: 21 December 2016 Accepted: 6 January 2017 Published: 30 January 2017

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Abstract

Motion detection is a live issue. Moving objects are an important clue for smart video surveillance systems. In this work we try to detect the motion in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. We are interested by objects that have some geometric shape (circle, ellipse, square, and rectangle). Proposed approaches are based on background subtraction and edge detection. Proposed algorithms mainly consist of three steps: edge detection, extracting objects with some geometric shapes and motion detection of extracted objects.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 2, Issue 1)
DOI	10.11648/j.ajdmkd.20170201.12
Page(s)	8-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Video Surveillance, Motion Detection, Real-Time System, Pattern Recognition

References

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[2]	K. Amaleswarao, G. Vijayadeep, and U. Shivaji, Improved Background Matching Framework for Motion Detection. International Journal of Computer Trends and Technology (IJCTT), 2013. 4 (8): p. 2873-2877.
[3]	T. Deepika and P. S. Babu, Motion Detection In Real-Time Video Surveillance With Movement Frame Capture And Auto Record. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), 2014. 3 (1): p. 146-149.
[4]	Singh, B., et al. Motion detection for video surveillance. in Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on. 2014.
[5]	Chen, B. H. and S. C. Huang, An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks. IEEE Transactions on Multimedia, 2014. 16 (3): p. 837-847.
[6]	Brutzer, S., et al. Evaluation of background subtraction techniques for video surveillance. in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. 2011.
[7]	Bari, N., N. Kamble, and P. Tamhankar, Android based object recognition and motion detection to aid visually impaired. International Journal of Advances in Computer Science and Technology, 2014. 3 (10): p. 462-466.
[8]	Seo, J. W. and S. D. Kim, Recursive On-Line and Its Application to Long-Term Background Subtraction. IEEE Transactions on Multimedia, 2014. 16 (8): p. 2333-2344.
[9]	Yun, K., et al. Motion Interaction Field for Accident Detection in Traffic Surveillance Video. in 22nd International Conference on Pattern Recognition (ICPR). 2014. Stockholm, Sweden.
[10]	Foggia, P., A. Saggese, and M. Vento, Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion. IEEE Transactions on Circuits and Systems for Video Technology, 2015. 25 (9): p. 1545-1556.
[11]	Nicolas, V., Suivi d'objets en mouvement dans une séquence vidéo. 2007, Centre universitaire des Saints-Pères.
[12]	Boudjemma, A., Estimation du mouvement dans une séquence d'images par approche probabiliste. 2011, University of Mouloud MAMMERI, Tizi-Ouzou.
[13]	BOUIROUGA, H., Reconnaissance des scènes vidéo pour adulte. 2012, Université Mohammed V, Agdal, Moroco.
[14]	Balakrishnan, An improved motion detection and tracking of active blob for video surveillance, in Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). 2013: Tiruchengode, India. p. 1-7.
[15]	Benezeth, Y., et al. Review and evaluation of commonly-implemented background subtraction algorithms. in International Conference on Pattern Recognition. 2008. Tampa, United States: IEEE.
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APA Style

Larbi Guezouli, Hanane Belhani. (2017). Motion Detection of Some Geometric Shapes in Video Surveillance. American Journal of Data Mining and Knowledge Discovery, 2(1), 8-14. https://doi.org/10.11648/j.ajdmkd.20170201.12

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ACS Style

Larbi Guezouli; Hanane Belhani. Motion Detection of Some Geometric Shapes in Video Surveillance. Am. J. Data Min. Knowl. Discov. 2017, 2(1), 8-14. doi: 10.11648/j.ajdmkd.20170201.12

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AMA Style

Larbi Guezouli, Hanane Belhani. Motion Detection of Some Geometric Shapes in Video Surveillance. Am J Data Min Knowl Discov. 2017;2(1):8-14. doi: 10.11648/j.ajdmkd.20170201.12

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@article{10.11648/j.ajdmkd.20170201.12,
  author = {Larbi Guezouli and Hanane Belhani},
  title = {Motion Detection of Some Geometric Shapes in Video Surveillance},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {2},
  number = {1},
  pages = {8-14},
  doi = {10.11648/j.ajdmkd.20170201.12},
  url = {https://doi.org/10.11648/j.ajdmkd.20170201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20170201.12},
  abstract = {Motion detection is a live issue. Moving objects are an important clue for smart video surveillance systems. In this work we try to detect the motion in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. We are interested by objects that have some geometric shape (circle, ellipse, square, and rectangle). Proposed approaches are based on background subtraction and edge detection. Proposed algorithms mainly consist of three steps: edge detection, extracting objects with some geometric shapes and motion detection of extracted objects.},
 year = {2017}
}

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TY  - JOUR
T1  - Motion Detection of Some Geometric Shapes in Video Surveillance
AU  - Larbi Guezouli
AU  - Hanane Belhani
Y1  - 2017/01/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajdmkd.20170201.12
DO  - 10.11648/j.ajdmkd.20170201.12
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 8
EP  - 14
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20170201.12
AB  - Motion detection is a live issue. Moving objects are an important clue for smart video surveillance systems. In this work we try to detect the motion in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. We are interested by objects that have some geometric shape (circle, ellipse, square, and rectangle). Proposed approaches are based on background subtraction and edge detection. Proposed algorithms mainly consist of three steps: edge detection, extracting objects with some geometric shapes and motion detection of extracted objects.
VL  - 2
IS  - 1
ER  -

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Author Information

Larbi Guezouli

LaSTIC Laboratory, Department of Computer Science, University of Batna, Batna, Algeria
Hanane Belhani

LaSTIC Laboratory, Department of Computer Science, University of Batna, Batna, Algeria

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APA Style

Larbi Guezouli, Hanane Belhani. (2017). Motion Detection of Some Geometric Shapes in Video Surveillance. American Journal of Data Mining and Knowledge Discovery, 2(1), 8-14. https://doi.org/10.11648/j.ajdmkd.20170201.12

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ACS Style

Larbi Guezouli; Hanane Belhani. Motion Detection of Some Geometric Shapes in Video Surveillance. Am. J. Data Min. Knowl. Discov. 2017, 2(1), 8-14. doi: 10.11648/j.ajdmkd.20170201.12

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AMA Style

Larbi Guezouli, Hanane Belhani. Motion Detection of Some Geometric Shapes in Video Surveillance. Am J Data Min Knowl Discov. 2017;2(1):8-14. doi: 10.11648/j.ajdmkd.20170201.12

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@article{10.11648/j.ajdmkd.20170201.12,
  author = {Larbi Guezouli and Hanane Belhani},
  title = {Motion Detection of Some Geometric Shapes in Video Surveillance},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {2},
  number = {1},
  pages = {8-14},
  doi = {10.11648/j.ajdmkd.20170201.12},
  url = {https://doi.org/10.11648/j.ajdmkd.20170201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20170201.12},
  abstract = {Motion detection is a live issue. Moving objects are an important clue for smart video surveillance systems. In this work we try to detect the motion in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. We are interested by objects that have some geometric shape (circle, ellipse, square, and rectangle). Proposed approaches are based on background subtraction and edge detection. Proposed algorithms mainly consist of three steps: edge detection, extracting objects with some geometric shapes and motion detection of extracted objects.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Motion Detection of Some Geometric Shapes in Video Surveillance
AU  - Larbi Guezouli
AU  - Hanane Belhani
Y1  - 2017/01/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajdmkd.20170201.12
DO  - 10.11648/j.ajdmkd.20170201.12
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 8
EP  - 14
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20170201.12
AB  - Motion detection is a live issue. Moving objects are an important clue for smart video surveillance systems. In this work we try to detect the motion in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. We are interested by objects that have some geometric shape (circle, ellipse, square, and rectangle). Proposed approaches are based on background subtraction and edge detection. Proposed algorithms mainly consist of three steps: edge detection, extracting objects with some geometric shapes and motion detection of extracted objects.
VL  - 2
IS  - 1
ER  -

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