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Journal articles on the topic 'Surveillance distribuée'

Author: Grafiati

Published: 11 January 2025

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1

C N, Dr Sowmyarani, Lavanya Naik, and Sangeetha S. "Security Analysis on Surveillance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 2195–201. http://dx.doi.org/10.22214/ijraset.2023.55016.

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Abstract: Nowadays in every organization, office, home, shops it has become mandatory to have surveillance system. This is for safety purpose in order to avoid theft and in turn achieve security. Most of the surveillance systems are managed and monitored by third parties as they are the ones who distribute them to various organizations. Meanwhile in some cases, for example in hospital, patient’s data might be sent to third parties in order to find best diagnosis or medicines for the disease that is increasing.

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2

Velastin, S. A. "Editorial: Intelligent distributed surveillance systems." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 2 (2005): 191. http://dx.doi.org/10.1049/ip-vis:20059045.

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3

Detmold, Henry, Anton van den Hengel, Anthony Dick, Katrina Falkner, David S. Munro, and Ron Morrison. "Middleware for Distributed Video Surveillance." IEEE Distributed Systems Online 9, no. 2 (February 2008): 1. http://dx.doi.org/10.1109/mdso.2008.7.

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4

Pan, Jin Xue. "A Load Balancing Mechanism for Video Surveillance System." Advanced Materials Research 1049-1050 (October 2014): 2079–83. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.2079.

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In recent years, with the development of video surveillance systems, the cluster and load balancing technology need be applied to improve the system performance and the quality of service. In this paper, on the basis of common load balancing algorithms, considering the characteristics of the video surveillance system, design a new load balancing scheduling mechanism, by improving the weighted round robin algorithm and introducing the nodes cooperation strategy. The testing results show that the new mechanism can distribute the loads more reasonably and make use of the server resources more effectively.

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5

Valera, M., and S. A. Velastin. "Intelligent distributed surveillance systems: a review." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 2 (2005): 192. http://dx.doi.org/10.1049/ip-vis:20041147.

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6

Kavalionak, Hanna, Claudio Gennaro, Giuseppe Amato, Claudio Vairo, Costantino Perciante, Carlo Meghini, and Fabrizio Falchi. "Distributed Video Surveillance Using Smart Cameras." Journal of Grid Computing 17, no. 1 (October 25, 2018): 59–77. http://dx.doi.org/10.1007/s10723-018-9467-x.

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7

Comaniciu, Dorin, Fabio Berton, and Visvanathan Ramesh. "Adaptive Resolution System for Distributed Surveillance." Real-Time Imaging 8, no. 5 (October 2002): 427–37. http://dx.doi.org/10.1006/rtim.2002.0298.

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8

Pennisi, A., F. Previtali, F. Ficarola, D. D. Bloisi, L. Iocchi, and A. Vitaletti. "Distributed Sensor Network for Multi-robot Surveillance." Procedia Computer Science 32 (2014): 1095–100. http://dx.doi.org/10.1016/j.procs.2014.05.538.

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9

Remagnino, P., A. I. Shihab, and G. A. Jones. "Distributed intelligence for multi-camera visual surveillance." Pattern Recognition 37, no. 4 (April 2004): 675–89. http://dx.doi.org/10.1016/j.patcog.2003.09.017.

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10

Farley, Ryan, and Xinyuan Wang. "Roving bugnet: Distributed surveillance threat and mitigation." Computers & Security 29, no. 5 (July 2010): 592–602. http://dx.doi.org/10.1016/j.cose.2009.12.002.

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11

Bramberger, M., A. Doblander, A. Maier, B. Rinner, and H. Schwabach. "Distributed Embedded Smart Cameras for Surveillance Applications." Computer 39, no. 2 (February 2006): 68–75. http://dx.doi.org/10.1109/mc.2006.55.

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12

Galloway, Alexander R. "“Carnivore personal edition”: exploring distributed data surveillance." AI & SOCIETY 20, no. 4 (March 9, 2006): 483–92. http://dx.doi.org/10.1007/s00146-006-0034-9.

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13

Ivanov, Yurii, Borys Sharov, Nazar Zalevskyi, and Ostap Kernytskyi. "Software System for End-Products Accounting in Bakery Production Lines Based on Distributed Video Streams Analysis." Advances in Cyber-Physical Systems 7, no. 2 (December 16, 2022): 101–7. http://dx.doi.org/10.23939/acps2022.02.101.

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Among the main requirements of modern surveillance systems are stability in the face of negative influences and intellectualization. The purpose of intellectualization is that the surveillance system should perform not only the main functions such as monitoring and stream recording but also have to provide effective stream processing. The requirement for this processing is that the system operation has to be automated, and the operator's influence should be minimal. Modern intelligent surveillance systems require the development of grouping methods. The context of the grouping method here is associated with a decomposition of the target problem. Depending on the purpose of the system, the target problem can represent several subproblems, each of which usually accomplishes by artificial intelligence or data mining methods.

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14

ZAVOLODKO, Ganna, Daria PAVLOVA, Yana KOLESNIKOVA, and Maksym SUKMANSKYI. "INTERSTAGE OPTIMIZATION OF DATA PROCESSING OF DISTRIBUTED AIRSPACE MONITORING SYSTEMS." ITSynergy, no. 1 (November 30, 2021): 58–65. http://dx.doi.org/10.53920/its-2021-1-7.

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The synthesis and analysis of the data processing optimal structure of survey radar surveillance systems are carried out in the work. By creating a temporary information database of signaling data for the required number of surveillance radar surveillance system, each element of which stores signaling data and quality indicators and parameters of their production, it is possible to carry out interstage optimization of airspace surveillance data processing based on Neumann-Pearson test. It will be possible to formulate the preparation of information messages faster within the current information, which will significantly affect the quality of the decision.

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15

Benaskeur, Abderrezak, Alaa Khamis, and Hengameh Irandoust. "Cooperation in distributed surveillance systems for dense regions." International Journal of Intelligent Defence Support Systems 4, no. 1 (2011): 20. http://dx.doi.org/10.1504/ijidss.2011.037806.

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16

Valera, M., S. A. Velastin, A. Ellis, and J. Ferryman. "Communication Mechanisms and Middleware for Distributed Video Surveillance." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 12 (December 2011): 1795–809. http://dx.doi.org/10.1109/tcsvt.2011.2133850.

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17

Kim, Junguk L., and Hector J. Hernandez. "A network surveillance protocol for distributed database systems." Data & Knowledge Engineering 6, no. 5 (September 1991): 409–20. http://dx.doi.org/10.1016/0169-023x(91)90010-u.

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18

McClure, Roderick J., and Karin Mack. "Injury surveillance as a distributed system of systems." Injury Prevention 22, Suppl 1 (December 29, 2015): i1—i2. http://dx.doi.org/10.1136/injuryprev-2015-041788.

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19

Albano, Pietro, Andrea Bruno, Bruno Carpentieri, Aniello Castiglione, Arcangelo Castiglione, Francesco Palmieri, Raffaele Pizzolante, Kangbin Yim, and Ilsun You. "Secure and distributed video surveillance via portable devices." Journal of Ambient Intelligence and Humanized Computing 5, no. 2 (April 19, 2013): 205–13. http://dx.doi.org/10.1007/s12652-013-0181-z.

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20

Hossain, M. Shamim. "QoS-aware service composition for distributed video surveillance." Multimedia Tools and Applications 73, no. 1 (February 1, 2013): 169–88. http://dx.doi.org/10.1007/s11042-012-1312-9.

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21

Chang, Ray-I., Te-Chih Wang, Chia-Hui Wang, Jen-Chang Liu, and Jan-Ming Ho. "Effective distributed service architecture for ubiquitous video surveillance." Information Systems Frontiers 14, no. 3 (August 14, 2010): 499–515. http://dx.doi.org/10.1007/s10796-010-9255-z.

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22

Watfa, Mohamed K., Leena Abdullah Alghamdi, and Manal Omer Bin Hamza. "Optimal deployment of a distributed IoT acoustic surveillance system." International Journal of Sensor Networks 37, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijsnet.2021.10041423.

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23

Hamza, Manal Omer Bin, Leena Abdullah Alghamdi, and Mohamed K. Watfa. "Optimal deployment of a distributed IoT acoustic surveillance system." International Journal of Sensor Networks 37, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijsnet.2021.117960.

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24

Wettergren, Thomas A., and Russell Costa. "Optimal Planning of Distributed Sensor Layouts for Collaborative Surveillance." International Journal of Distributed Sensor Networks 9, no. 4 (April 2013): 145496. http://dx.doi.org/10.1155/2013/145496.

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25

Fujita, Hiroyuki, and Kazuyuki Aihara. "A Distributed Surveillance and Protection System in Living Organisms." IEEJ Transactions on Electronics, Information and Systems 107, no. 11 (1987): 1042–48. http://dx.doi.org/10.1541/ieejeiss1987.107.11_1042.

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26

Wang, Xiaopeng, and Zihuai Lin. "Microwave Surveillance Based on Ghost Imaging and Distributed Antennas." IEEE Antennas and Wireless Propagation Letters 15 (2016): 1831–34. http://dx.doi.org/10.1109/lawp.2016.2538787.

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27

Zhang, Guoxian, Gregory K. Fricke, and Devendra P. Garg. "Spill Detection and Perimeter Surveillance via Distributed Swarming Agents." IEEE/ASME Transactions on Mechatronics 18, no. 1 (February 2013): 121–29. http://dx.doi.org/10.1109/tmech.2011.2164578.

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28

Saptharishi, M., C. Spence Oliver, C. P. Diehl, K. S. Bhat, J. M. Dolan, A. Trebi-Ollennu, and P. K. Khosla. "Distributed surveillance and reconnaissance using multiple autonomous ATVs: CyberScout." IEEE Transactions on Robotics and Automation 18, no. 5 (October 2002): 826–36. http://dx.doi.org/10.1109/tra.2002.804501.

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29

Xue Wang, Sheng Wang, and Daowei Bi. "Distributed Visual-Target-Surveillance System in Wireless Sensor Networks." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 5 (October 2009): 1134–46. http://dx.doi.org/10.1109/tsmcb.2009.2013196.

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30

Mostafaei, Habib, Morshed U. Chowdhury, and Mohammad S. Obaidat. "Border Surveillance With WSN Systems in a Distributed Manner." IEEE Systems Journal 12, no. 4 (December 2018): 3703–12. http://dx.doi.org/10.1109/jsyst.2018.2794583.

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31

Coifman, Benjamin A., and Ramachandran Mallika. "Distributed surveillance on freeways emphasizing incident detection and verification." Transportation Research Part A: Policy and Practice 41, no. 8 (October 2007): 750–67. http://dx.doi.org/10.1016/j.tra.2006.12.001.

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32

Velastin, Sergio A., Benny Lo, and Jie Sun. "A flexible communications protocol for a distributed surveillance system." Journal of Network and Computer Applications 27, no. 4 (November 2004): 221–53. http://dx.doi.org/10.1016/j.jnca.2003.11.001.

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33

Walter, B. "A turnable protocol for symmetric surveillance in distributed systems." ACM SIGCOMM Computer Communication Review 16, no. 3 (August 1986): 368–76. http://dx.doi.org/10.1145/1013812.18213.

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34

Rahman, Sk Md Mizanur, M. Anwar Hossain, Mohammad Mehedi Hassan, Atif Alamri, Abdullah Alghamdi, and Mukaddim Pathan. "Secure privacy vault design for distributed multimedia surveillance system." Future Generation Computer Systems 55 (February 2016): 344–52. http://dx.doi.org/10.1016/j.future.2014.10.019.

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35

Lo Presti, Liliana, Stan Sclaroff, and Marco La Cascia. "Path Modeling and Retrieval in Distributed Video Surveillance Databases." IEEE Transactions on Multimedia 14, no. 2 (April 2012): 346–60. http://dx.doi.org/10.1109/tmm.2011.2173323.

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36

Nam, Yunyoung, Sangjin Hong, and Seungmin Rho. "Data modeling and query processing for distributed surveillance systems." New Review of Hypermedia and Multimedia 19, no. 3-4 (December 2013): 299–327. http://dx.doi.org/10.1080/13614568.2013.849762.

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37

Chang, Che-Cheng, and Jichiang Tsai. "Distributed collaborative surveillance system based on leader election protocols." IET Wireless Sensor Systems 6, no. 6 (December 1, 2016): 198–205. http://dx.doi.org/10.1049/iet-wss.2015.0030.

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38

Mnatsakanyan, Zaruhi R., Howard S. Burkom, Mohammad R. Hashemian, and Michael A. Coletta. "Distributed information fusion models for regional public health surveillance." Information Fusion 13, no. 2 (April 2012): 129–36. http://dx.doi.org/10.1016/j.inffus.2010.12.002.

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39

Ramos, Fernando M. S., and Filipe M. Patrı́cio. "Application of Distributed Platforms in a Video Surveillance System." Real-Time Imaging 7, no. 5 (October 2001): 447–55. http://dx.doi.org/10.1006/rtim.2000.0212.

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40

Svyd, I. V., and S. V. Starokozhev. "Distributed processing of radar information in airspace surveillance systems." Radiotekhnika, no. 212 (March 28, 2023): 155–65. http://dx.doi.org/10.30837/rt.2023.1.212.15.

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The work is dedicated to the analysis of the quality of combining assessments of the radar signals and airborne objects detections in the implementation of distributed processing of radar information of airspace surveillance systems. The main sources of radar information about the air situation in the airspace control system are primary surveillance radars, secondary radar systems and identification systems on the basis of "friend or foe". It should be noted that the analysis of the information security of single-position radars shows their vulnerability in a wide range of unintentional and intentional interference, as well as determining their location. This is due to the ease of detection of the emitting transmitter of the probing signal in single-position radars. It led to the main disadvantage of single-position radars – low noise immunity and low survivability. The transition to a network of radar systems can significantly reduce the impact of deliberately directed interference. It also allows the use of methods for distributed processing of radar information in airspace surveillance systems. Analysis of the effectiveness of information support algorithms based on distributed processing of radar information of airspace surveillance systems, taking into account the final result, makes it possible to detect airborne objects using a packet of binary-quantized signals, taking into account two algorithms for combining detection results: channel accumulation and combining results; association of channel solutions and accumulation. It shows following: – the quality of consumer information support based on the proposed structure is much higher compared to the used radar information processing structure; the quality of information support for consumers has the best performance when using the signal processing method based on the accumulation of signals with the subsequent combination of detection results; the availability factor of the aircraft transponder significantly affects the quality of information support, already at P0<0.9 the use of integer logic for combining detection information is undesirable.

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41

Cheng, Xiaoling, Mengxi Xu, Xijun Yan, Yang Yang, Yue Xu, and Yinglan Ruan. "A Design Pattern of IAPVS Platform Based on Distributed Edge Computing." Journal of Physics: Conference Series 2732, no. 1 (March 1, 2024): 012001. http://dx.doi.org/10.1088/1742-6596/2732/1/012001.

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Abstract Traditional video surveillance is difficult to effectively cope with real-time interaction between end side devices of over ten thousand scale and data centers, and these devices are widely dispersed and deployed on a large scale. They cannot meet the needs of diverse scenarios monitoring and smart applications such as security, urban comprehensive management, ports, mines, water conservancy, power, industrial manufacturing and other fields. Based on the analysis of the problems in the popular intelligent video surveillance system, this paper proposes the design mode of an intelligent video surveillance intelligent analysis platform (IAPVS) based on distributed edge computing by improving the existing centralized video cloud computing architecture.This design pattern provides a new overall design solution for the development of platform of intelligent video analysis suitable for diverse scenarios and smart application fields.

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42

Yang, Biao, Guo Yu Lin, and Wei Gong Zhang. "An Embedded System Used as Intelligent Node of Distributed Surveillance." Applied Mechanics and Materials 475-476 (December 2013): 763–66. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.763.

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The demand for intelligent surveillance is grown with the popularization of the camera monitoring network. An embedded system used as intelligent node of distributed surveillance is designed in this paper to improve the intelligent level of camera monitoring. Target detection and tracking can be implemented in this node and an anomaly intrusion detection system is designed based on the tracking results. Information sharing is realized via transmitting highly abstract object descriptors via the wireless network. A simple camera monitoring network is built in the labs to test the designed intelligent node and the experimental results indicate its effectiveness and accuracy.

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43

Galimov, R. R., and A. Yu Kruchinin. "Optimal Placement of Cameras in a Distributed Video Surveillance System." Bulletin of Kalashnikov ISTU 21, no. 3 (October 16, 2018): 192. http://dx.doi.org/10.22213/2413-1172-2018-3-192-197.

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Актуальность темы определяется широким развитием распределенных систем видеонаблюдения, характеризующихся большим количеством видеокамер, расположенных на большой территории. Эффективность распределенных систем видеонаблюдения зависит от множества факторов, таких как используемые алгоритмы распознавания, технические характеристики камер и средств обработки данных. Кроме того, качество системы видеонаблюдения во многом зависит от топологии размещения видеокамер. Целевая функция минимизирует количество камер, обеспечивая при этом необходимый уровень достоверности распознавания событий. Считается, что качество распознавания в основном зависит от пространственного разрешения распознанного объекта на видеокадре. Этот параметр определяется техническими характеристиками камеры и расстоянием до распознанного объекта. При пересечении поля зрения нескольких камер и возможности комбинирования их результатов увеличивается оценка достоверности распознавания объектов. Таким образом, при размещении камер в контролируемой зоне необходимо учитывать множество параметров: координаты, углы обзора, области пересечения поля зрения камер. Данная задача характеризуется высокой вычислительной сложностью. Поэтому для получения результата за приемлемое время предложен алгоритм оптимизации топологии на основе метода роя частиц. Представленный подход к оптимизации топологии размещения камер сократит время проектирования системы видеонаблюдения.

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44

Kumar, B. Prasanth, and Y. Bhaskarrao. "A Distributed Framework for Surveillance Missions Robots to Detect Intruders." Indian Journal of Public Health Research & Development 8, no. 4 (2017): 1080. http://dx.doi.org/10.5958/0976-5506.2017.00471.5.

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45

Uddin, Md Azher, Aftab Alam, Nguyen Anh Tu, Md Siyamul Islam, and Young-Koo Lee. "SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance." Symmetry 11, no. 7 (July 12, 2019): 911. http://dx.doi.org/10.3390/sym11070911.

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In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

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46

Julian, Kyle D., and Mykel J. Kochenderfer. "Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning." Journal of Guidance, Control, and Dynamics 42, no. 8 (August 2019): 1768–78. http://dx.doi.org/10.2514/1.g004106.

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47

Lamichhane, Kamal. "IP Based Distributed Smart Camera Surveillance System for Forest Application." i-manager's Journal on Digital Signal Processing 1, no. 3 (September 15, 2013): 14–16. http://dx.doi.org/10.26634/jdp.1.3.2438.

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48

Marcenaro, L., F. Oberti, G. L. Foresti, and C. S. Regazzoni. "Distributed architectures and logical-task decomposition in multimedia surveillance systems." Proceedings of the IEEE 89, no. 10 (2001): 1419–40. http://dx.doi.org/10.1109/5.959339.

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49

Bousetouane, F., F. Vandewiele, and C. Motamed. "Occlusion management in distributed multi-object tracking for visual-surveillance." Pattern Recognition and Image Analysis 25, no. 2 (April 2015): 295–300. http://dx.doi.org/10.1134/s1054661815020042.

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50

Favorskaya, Margarita, Euvgenii Kazmiruk, and Aleksei Popov. "Distributed System for Crossroads Traffic Surveillance with Prediction of Incidents." Procedia Computer Science 35 (2014): 851–60. http://dx.doi.org/10.1016/j.procs.2014.08.252.

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