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  • مکان گزینی و بهینه سازی پوشش دوربین های مدار بسته به منظور حمایت از مونیتورینگ بهتر با استفاده از الگوریتم S-ROPE و بصری سازی سه بعدی

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کد مقاله : JEST-1807-4361 (R2) بازدید : 74 صفحه: 89 - 102

10.22034/jest.2019.37346.4361

نوع مقاله: پژوهشی

مکان گزینی و بهینه سازی پوشش دوربین های مدار بسته به منظور حمایت از مونیتورینگ بهتر با استفاده از الگوریتم S-ROPE و بصری سازی سه بعدی

محورهای موضوعی : سیستم اطلاعات جغرافیایی

جعفر کریمی 1 (کارشناس ارشد سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران.)
محمد حسن وحیدنیا 2 (استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، (مسوول مکاتبات))

تاریخ دریافت : 1397/04/10 تاریخ پذیرش : 1397/08/02 تاریخ انتشار : 1399/08/01

کلید واژه: خط دید و تحلیل محدوده دید, دوربین های مدار بسته, بصری سازی سه بعدی, S-ROPE روش ساده رتبه بندی و حذف همپوشانی,

چکیده مقاله :

زمینه و هدف: در جوامع مدرن مونیتورینگ براساس دوربین های مداربسته مسئله ای ضروری برای حفظ محیط زیست و امنیت اجتماعی به شمار می آید. بهینه سازی شبکه های دوربین و نحوه طراحی شبکه آن ها از جمله چالش های مطالعات شبکه های دوربین است. هدف از این مقاله توسعه شیوه ای مکانمند برای یافتن پیکربندی دوربین های مداربسته جهت ایجاد حداکثر پوشش تصویری ممکن در یک فضای شهری است. روش بررسی: به طور کلی این پژوهش در دو مرحله در سال 96 انجام گرفته است. در مرحله اول اعمال الگوریتم مکانیابی دوربین ها در فضای دو بعدی صورت می پذیرد و خروجی به دست آمده از آن در مرحله دوم در فضای سه بعدی و به صورت بصری تجزیه و تحلیل می گردد. اولین مرحله با استفاده از نرم افزار ArcGIS و زبان برنامه نویسی Python انجام پذیرفت و از الگوریتم S-ROPE به عنوان روشی با میزان دقت بالا در زمینه استقرار دوربین ها استفاده شد. ضمن این که تعدیلاتی در الگوریتم شامل زاویه دید و غیر دودویی بودن منطقه انجام گرفت. در مرحله دوم از مدلسازی سه بعدی در محیطcity Engine برای اعتبارسنجی خروجی به دست آمده در یک محدوده شهری استفاده شد. یافته ها: با الگوریتم S-ROPE یک مکان گزینی اتوماتیک برای دوربین ها انجام گرفت به گونه ای که مساحت 28/1798 متر مربع از کل مساحت 98/1953 مترمربعی محدوده مطالعاتی، یعنی 92%، تحت پوشش قرار گرفت. پس از بررسی سه بعدی تنها دو عدد دوربین به مجموع دوربین ها برای پوشش 100% اضافه شد. بحث و نتیجه گیری: با روش پیشنهادی اولاً در تعداد دوربین های به کار رفته به تعداد قابل توجهی صرفه جویی صورت می پذیرد و در عین حال بیشترین پوشش ممکن نیز به دست خواهد آمد.تنها چالش پیش رو زمان اجرای فرآیند برای مساحت های بزرگ می باشد که با توجه به غیر آنی بودن ماهیت مسئله، خللی در روش پیشنهادی ایجاد نمی کند.

چکیده انگلیسی:

Background and Objective: Modern surveillance systems based on CCTV cameras is an essential element for protecting the environment and social security. Camera network optimization and designing its architecture are among the issues of camera network studies. The purpose of this paper is to develop a geospatial solution to find configurations for CCTV cameras in such a way that creates the maximum possible visual coverage in an urban area. Methods: In general, this research is performed in two steps. In the first step, the algorithm is used to locate cameras in two-dimensional space, and the resulting output is analyzed in the second step in a three-dimensional space and visually. The first step was performed using ArcGIS software and Python programming language, and the S-ROPE algorithm was used as a high-precision method for 2D camera deployment. After the modifications were made at the viewing and non-binary regions of the region, the location of the cameras was determined. In the second stage, the three-dimensional model of City Engine software was used to validate the output obtained using the S-ROPE algorithm. The evaluation of the applied method was performed on an urban study area. Findings: With the S-ROPE algorithm, an automated location determination for cameras was taken so that the area of 1798.28 m² was covered by a total area of 1953.98 m² of study area, i.e. 92%. After a three-dimensional review, only two cameras were added to the total of cameras to cover 100%. Discussion and Conclusion: With the proposed method, the number of cameras used makes significant savings, and the most possible coverage is achieved. The only challenge is the process time for large areas, which, due to the non-urgent nature of the problem, does not create a dent in the proposed method.  

منابع و مأخذ:


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_||_

  1. Ratcliffe, J.H., Taniguchi, T., Taylor, R.B., 2009. The Crime Reduction Effects of Public CCTV Cameras: A Multi-Method Spatial Approach. Justice Quarterly, Vol. 26, pp. 4746-770.
    2.
  2. Jalali, G., Pirhouri, H., 2016. Developing smart city strategies from a passive defense perspective using SWOT analysis with an emphasis on transportation. First International Conference on Urban Economics (with Respectable Economic Approach and Action). Scientific Society of Urban Economics of Iran pp. 899-910, Tehran, Iran(Persian).
    3.
  3. Welsh, B.C., Farrington, D.P., 2002. Crime prevention effects of closed circuit television: a systematic review. Home Office Research Study Number 252. Home Office, London.
    4.
  4. Chvatal, V., 1975. A combinatorial theorem in plane geometry. Journal of Combinatorial Theory, Vol. 18, pp.39-41.
    5.
  5. Fisk, S., 1999. A Short Proof of Chvatal’s Watchman Theorem. Journal of Combinatorial Theory, Vol. 24, pp. 374-375.
    6.
  6. Bose, S.P., Toussaint, G., Zhu, B., 1997. Guarding polyhedral terrains. Computational Geometry, Vol. 1, pp. 173–185.
    7.
  7. Marengoni, M., Draper, B., Hanson, A., Sitaraman, R., 2000. A system to place observers on a polyhedral terrain in polynomial time. Image and Vision Computing, Vol. 18, 773–780.
    8.
  8. Cole, R., Sharir, M., 1989. Visibility problems for polygedral terrain. Journal of Symbolic Computation, Vol. 17, pp. 11–30.
    9.
  9. Eidenbenz, S., 2002. Approximation algorithms for terrain guarding. Information Processing Letters, Vol. 82, pp. 99–105.
    10.
  10. Erdem, U., Sclaroff, S., 2004. Optimal placement of cameras in floorplan to satisfy task requirements and Classical Cameras. Proceedings of the fifth Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras. Prague, Czech Republic.
    11.
  11. Kazazakis, G., Argyros, A., 2002. Fast positioning of limited-visibility guards for the inspection of 2D Workspaces. Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2843–2848. Lausanne, Switzerland.
    12.
  12. Floriani, L., Falcidieno, B., Pienovi, C., Allen, D., Nagy, G., 1986. A visibility-based model for terrain features. Proceedings, Second International Symposium on Spatial Data Handling, (pp. 235–250). Seattle, WA, USA.
    13.
  13. Goodchild, M., Lee, J., 1989. Coverage problems and visibility regions on topographic surfaces. Annals of Operations Research, Vol. 18, pp. 175–186.
    14.
  14. Lee, J., 1991. Analyses of visibility sites on topographic surfaces. International Journal of Geographical Information Systems, Vol. 5, 413-429.
    15.
  15. Kim, Y., Rana, S., Wise, S., 2004. Exploring multiple viewshed analysis using terrain features and optimisation techniques. Computers & Geosciences, Vol. 30, pp. 1019–1032.
    16.
  16. Kaucic, B., Zalik, B., 2004. K-guarding of polyhedral terrain. International Journal of Geographical Information Science, Vol. 18, pp. 709–718.
    17.
  17. Pavlidis, I., Morellas, V., Tsiamyrtzia, P., Harp, S., 2001. Urban surveillance systems: From the laboratory to the commercial world. Proceedings of the IEEE, pp. 1478–1497.
    18.
  18. Valera, M., Velastin, S., 2001. Intelligent distributed surveillance systems: A review. IEEE Proceedings Vision, Image and Signal Processing, Vol. 152, pp. 192–204.
    19.
  19. Bocca, E., Viazzo, S., Longo, F., Mirabelli, G., 2005. Developing data fusion systems devoted to security control in port facilities. In Proceedings of the 2005 Winter Simulation Conference, pp. 445-449. Orlando, FL, USA.
    20.
  20. Ercan, A., Yang, D., El Gamal, A., Guibas, L., 2006. Optimal placement and selection of camera network nodes for target localization. In Proc. IEEE Int. Conf. Distributed Computing in Sensor System, 2006, vol. 4026, pp. 389–404.
    21.
  21. Bodor, R., Drenner, A., Schrater, P., Papanikolopoulos, N., 2007. Optimal camera placement for automated surveillance tasks. Journal of Intelligent and Robotic Systems, Vol. 50, pp. 257–295.
    22.
  22. Fehr, D., Fiore, L., Papanikolopoulos, N., 2009. Issues and solutions in surveillance camera placement. In Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pp. 3780–3785. St. Louis, MO, USA.
    23.
  23. Ghosh, S., 2010. Approximation algorithms for art gallery problems in polygons. Discrete Applied Mathematics, Vol. 158, pp. 718-722.
    24.
  24. Yaagoubi, R., El Yarmani, M., Kamel, A., Khemiri, W., 2015. HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement. ISPRS International Journal of Geo-Information, Vol. 4, pp. 754-782.
    25.
  25. de Floriani, L., Magillo, P., 2003. Algorithms for visibility computation on terrains: A survey. Environment and Planning B: Planning and Design, Vol. 30, pp. 709–728.
    26.
  26. Choi, K., Lee, I., 2015. CCTV coverage index based on surveillance resolution and its evaluation using 3D spatial analysis. Sensors, Vol. 15, pp. 23341-23360.
    27.
  27. Vahidnia, M.H., Vafaeinejad, A., Shafiei, M., 2019. Heuristic game-theoretic equilibrium establishment with application to task distribution among agents in spatial networks. Journal of Spatial Science, Vol. 64, pp. 131-152.
    28.

 

 

 




 

 

 

 

 

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