Signal and Data Processing

fa انتخاب بهترین مسیرهای نصب در توسعه دوربین‌های مداربسته شهری Choosing the Best Installation Paths In the Development of Urban CCTV Cameras مقالات پردازش تصویر Paper پژوهشي Research <div style="text-align: justify;">دوربین&lrm;های مداربسته یکی از مهم‌ترین ابزارهایی است که در شهرها برای سامانه کنترل ترافیک استفاده می‌شود. شهروندان روزانه مسافرت‌های درون‌شهری زیادی انجام می‌دهند و عملکرد سامانه‌های نظارت شهری نیز پایش این مسیرهاست. در روش پیشنهادی، نقشه واقعی شهر به‌عنوان یک مدل انتخاب شده‌است. با جداسازی مسیر‌های اصلی از سایر مکان‌ها، گرافی از مسیرها به‌دست می‌آید؛ سپس با انتخاب تصادفی مجموعه‌ای از زوج‌رأس‌ها از گراف، به‌عنوان مبدأ و مقصد یک سفر داخل شهری و مسیریابی بین آن‌ها با الگوریتم دایکسترا، ترافیک مجازی ساخته می‌شود. برای تطابق ترافیک مجازی با ترافیک واقعی، احتمال انتخاب نقاط پر رفت‌و‌آمد بیشتر درنظرگرفته می‌شود. با ایجاد یک‌صدهزار مسیر برای مدل مورد مطالعه، می‌توان یال&lrm;ها را با بالاترین تکرار به‌عنوان نتایج نهایی پیدا کرد و برای نصب دوربین پیشنهاد داد. ارزیابی نتایج نهایی با تکرار آزمایش‌های تصادفی و با استفاده از ضریب تشابه ژاکارد انجام گرفته است و میزان تشابه نتایج خروجی بررسی می‌شود. پایایی روش پیشنهادی با تحلیل ریاضی و با رسم نمودارها بیان می‌شود و تأثیر پارامترهای تأثیرگذار مانند تعداد مسافرت شهری، میزان احتمال انتخاب نقاط، تأثیر توپولوژی شهر و تعداد نتایج خروجی به‌شکل تحلیلی بیان‌ شده و میزان تشابه نتایج، 98درصد به‌دست آمد. مزیت روش پیشنهادی وابسته‌نبودن به ابزار خاص مانند دوربین‌های سنجش ترافیک و همچنین بدون وابستگی به مکان و توپولوژی خاص است.</div> <div style="text-align: justify;">Optimizing camera placement is a two-decade-old research problem. Many researches have solved the problem with different approaches. Some different methods such as genetic algorithm, reinforcement learning, and greedy algorithm have been developed to obtain the maximum surface coverage. Some researchers have considered specific applications in order to optimally cover a certain area such as a coastal area or a protected area under the coverage of CCTV cameras. Some researchers have also considered the camera's capabilities of vertical rotation or horizontal rotation or zooming in order to use these capabilities for optimization. With the development of drone manufacturing technology, this tool is also proposed for specific applications. But what is less discussed is the optimization of the placement of urban surveillance cameras in a real city map. Usually, due to the high cost, all city cameras are not installed at once, and cameras are added annually to develop the city traffic monitoring system. Therefore, it is necessary to prioritize the selection of the route and a very important factor in prioritization is traffic. Traffic is the most important factor in choosing the route for the placement of urban surveillance cameras because the streets with more traffic are exposed to more traffic accidents and should be the priority for video monitoring. Traffic data is usually big data, not available for all cities, and on the other hand, providing traffic data may violate citizens' privacy. Therefore, there are many methods for creating virtual traffic, which are classified into two categories: macro and micro. Macro methods model traffic as a physical phenomenon such as fluid or gas, but micro models, which are mostly used in artificial intelligence methods, consider traffic as a set of individual trips. In this work, we use the second method to create virtual traffic so that routes with more traffic are prioritized for installation. Citizens usually make a lot of intra-city trips, and the function of city monitoring systems is to monitor these routes. Therefore, the placement of surveillance cameras should also be in such a way that it considers the observation of these routes. In the proposed method, the real map of the city is selected as a model. Then, by separating the main paths and obtaining the skeleton of the path, a graph of the paths is obtained, the intersection point of the paths will be its vertex and the distance between the vertices will be the weight of the connecting edges. Now by randomly selecting two vertices from the graph as the origin and destination of an intra-city trip and routing between them with Dijkstra's algorithm, a trip is made. By repeating this process, virtual traffic is simulated. To create virtual traffic similar to real traffic, the probability of choosing high-traffic points is considered more than other points. Therefore, the probability of selecting vertices in the graph is different according to their location in the city. By creating one hundred thousand paths for the studied model, the edges with the highest repetition can be found as the final results and suggested for camera installation. The evaluation of the final results is done by repeating random experiments and using the Jaccard similarity coefficient, and the degree of similarity of the output results is checked. The reliability of the proposed method is expressed by mathematical analysis and by drawing graphs, and the impact of influential parameters such as the number of city trips, the probability of choosing points, the impact of city topology, and the number of output results are expressed analytically, and the similarity of the results is 98%. The advantage of the proposed method is not depending on special tools such as special cameras for traffic measurement, as well as not depending on a specific location and topology.</div> جانمایی دوربین مداربسته شهری, ترافیک مجازی, ضریب تشابه ژاکارد, الگوریتم دایکسترا, شهر هوشمند placement of urban CCTV camera - Virtual traffic - Jaccard similarity coefficient - Dijkstra's algorithm - smart city 67 78 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-2460-1&slc_lang=fa&sid=1 hassan sanei arani حسن صانعی آرانی hassan.sanei@srbiau.ac.ir 100319475328460012999 100319475328460012999 Yes PHD Student, Department of Information Technology Management, Faculty of Management and Accounting, Islamic Azad University, Science and Research Branch دانشجوی دکترای مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران mahdi esmaili مهدی اسماعیلی m.esmaeili@iaukashan.ac.ir 100319475328460013000 100319475328460013000 No Assistant Professor, Computer Department, Faculty of Electrical and Computer Engineering, Islamic Azad University, Kashan Branch استادیار گروه کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه آزاد اسلامی واحد کاشان، کاشان، ایران Mohmmad ali Afshar kazimi محمد علی افشار کاظمی m.afsharkazemi@iauec.ac.ir 100319475328460013001 100319475328460013001 No Associate Professor, Department of Industrial Management, Faculty of Management and Accounting, Islamic Azad University, Tehran Center Branch دانشیار گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی واحد تهران مرکز، تهران، ایران