| Peer-Reviewed

Motion Detection of Some Geometric Shapes in Video Surveillance

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
[1] Ait Fares, W., Détection et suivi d'objets par vision fondés sur segmentation par contour actif basé région. 2013, Université Paul Sabatier - Toulouse III.
[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.
[16] RONSE, C. Opérations morphologiques de base: dilatation, érosion, ouverture et fermeture binaires. 2013 [cited 2016 9th of June 2016]; Available from: https://dpt-info.u-strasbg.fr/~cronse/TIDOC/MM/deof.html.
[17] John, C., A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986. 8 (6): p. 679-698.
[18] Chandana, S. Real time video surveillance system using motion detection. in 2011 Annual IEEE India Conference. 2011.
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Cite This Article
  • 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
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    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
  • LaSTIC Laboratory, Department of Computer Science, University of Batna, Batna, Algeria

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

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