Relevant bibliographies by topics / Video surveillance system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Video surveillance system'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Video surveillance system"

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Al-Asadi, Tawfiq A., and Salah H. Al-bohader. "Surveillance System based on GIS and Video Film." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 701–7. http://dx.doi.org/10.5373/jardcs/v11sp10/20192860.

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Praveen, Dabbara. "Intelligent Video Surveillance System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 741–43. http://dx.doi.org/10.22214/ijraset.2021.37399.

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Intelligent video recognition with in-depth learning concept will create a self-paced video analytics program. CCTV cameras are used in all areas where safety is paramount. Manual monitoring seems tedious and time-consuming. Security can be defined by different words in different contexts such as identity theft, violence, explosions etc. Security monitoring is a tedious and time-consuming task. In this project we will analyse video feeds in real time and identify any unusual items such as violence or theft. The concept of in-depth learning simulates the functioning of the human brain in processing data for use in acquisition, speech recognition, decision making, etc. This will depend without human guidance, from unstructured and unlabelled data.
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Agrawal, Priyanka. "Smart Surveillance System using Face Tracking." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2613–17. http://dx.doi.org/10.22214/ijraset.2021.35567.

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The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.
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Vaidya, Sonali, and Kamal Shah. "Real Time Video Surveillance System." International Journal of Computer Applications 86, no. 14 (January 16, 2014): 22–27. http://dx.doi.org/10.5120/15054-3419.

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Ali, Ahsan, Asghar Ali Shah, Kashif Aftab, and Muhammad Usman. "Multicamera Video Stitching Surveillance System." VAWKUM Transactions on Computer Sciences 16, no. 1 (November 27, 2018): 19. http://dx.doi.org/10.21015/vtcs.v16i1.544.

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Ganesan, K., C. Kavitha, Kriti Tandon, and R. Lakshmipriya. "Traffic Surveillance Video Management System." International journal of Multimedia & Its Applications 2, no. 4 (November 30, 2010): 28–36. http://dx.doi.org/10.5121/ijma.2010.2403.

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Patil, Mr Rahul V., and Mrs Bangar S. B. "Video Surveillance Based Attendance system." IJARCCE 6, no. 3 (March 30, 2017): 708–13. http://dx.doi.org/10.17148/ijarcce.2017.63167.

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Oswal, Sangeeta, and CV Ritu Ramesh. "Real Time Video Surveillance System." IOSR Journal of Computer Engineering 19, no. 03 (June 2017): 76–79. http://dx.doi.org/10.9790/0661-1903067679.

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An, Tae-Ki, and Moon-Hyun Kim. "Context-aware Video Surveillance System." Journal of Electrical Engineering and Technology 7, no. 1 (January 1, 2012): 115–23. http://dx.doi.org/10.5370/jeet.2012.7.1.115.

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Jung, Jongtack, Seungho Yoo, Woong La, Dongkyu Lee, Mungyu Bae, and Hwangnam Kim. "AVSS: Airborne Video Surveillance System." Sensors 18, no. 6 (June 14, 2018): 1939. http://dx.doi.org/10.3390/s18061939.

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Dissertations / Theses on the topic "Video surveillance system"

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Haida, Maksim, and Максим Володимирович Гайда. "Video surveillance system of target contour." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50751.

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1. Программирование компьютерного зрения на языке Python / Ян Эрик Солем, пер. с англ. Слинкин А.А. – М.: ДМК Пресс, 2016. – 312.с.: ил. 2. Системи відеоспостереження та методи виділення контурів на зображеннях / K. Гжешчик, Д. Загородня, А. Саченко, Б. Русин – «Управління проектами та розвиток виробництва», 2018, №3(67). 3. Advances in Computer Vision and Pattern Recognition / Giovanni Maria Farinella, Sebastiano Battiato, Roberto Cipolla – Springer-Verlag London 2013. 4. Детекторы углов. URL: https://habr.com/ru/post/244541/ (дата звернення: 23.02.2021).
Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. When recognizing objects, the most informative part of the image is the contour. An object contour is a part of an object that contains a lot of information about the shape of the object and almost does not depend on the color and texture of the image. You can analyze the shape of the object along the contour. In many cases, information about the shape of the object is sufficient to organize automated or automatic systems. In addition, the transition to object recognition by their contours allows to reduce the amount of processed information by several times, as a plus, the contours are invariant to the brightness transformations.The first element of an intelligent video surveillance system is a video sensor. Examples of video sensors are digital or IP cameras. For ease of use, installation and taking into account the cost of the module, I will use a WEB camera without an ultraviolet filter, with a resolution of 1280×720 pixels and a video recording rate of 25 frames per second.
Комп’ютерне бачення – це наука і технологія виготовлення машин, які бачать. Він займається теорією, розробкою та реалізацією алгоритмів, які можуть автоматично обробляти візуальні дані для розпізнавання об’єктів, відстеження та відновлення їх форми та просторового розташування. При розпізнаванні об’єктів найбільш інформативною частиною зображення є контур. Контур об’єкта - це частина об’єкта, що містить багато інформації про форму об’єкта і майже не залежить від кольору та текстури зображення. Ви можете проаналізувати форму предмета по контуру. У багатьох випадках інформації про форму предмета достатньо для організації автоматизованих або автоматичних систем. Крім того, перехід до розпізнавання об’єктів за їх контурами дозволяє зменшити оброблену інформацію в кілька разів, плюс, контури інваріантні до перетворень яскравості. Першим елементом інтелектуальної системи відеоспостереження є відеодатчик. Прикладами відеосенсорів є цифрові або IP-камери. Для зручності використання, встановлення та врахування вартості модуля я буду використовувати веб-камеру без ультрафіолетового фільтра, з роздільною здатністю 1280 × 720 пікселів та швидкістю запису відео 25 кадрів в секунду.
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Zou, Zichuan. "Remote Client of Home Video Surveillance System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459453065.

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Zhou, Han, and 周晗. "Intelligent video surveillance in a calibrated multi-camera system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45989217.

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Luo, Ning. "A Wireless Traffic Surveillance System Using Video Analytics." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68005/.

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Video surveillance systems have been commonly used in transportation systems to support traffic monitoring, speed estimation, and incident detection. However, there are several challenges in developing and deploying such systems, including high development and maintenance costs, bandwidth bottleneck for long range link, and lack of advanced analytics. In this thesis, I leverage current wireless, video camera, and analytics technologies, and present a wireless traffic monitoring system. I first present an overview of the system. Then I describe the site investigation and several test links with different hardware/software configurations to demonstrate the effectiveness of the system. The system development process was documented to provide guidelines for future development. Furthermore, I propose a novel speed-estimation analytics algorithm that takes into consideration roads with slope angles. I prove the correctness of the algorithm theoretically, and validate the effectiveness of the algorithm experimentally. The experimental results on both synthetic and real dataset show that the algorithm is more accurate than the baseline algorithm 80% of the time. On average the accuracy improvement of speed estimation is over 3.7% even for very small slope angles.
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Abdelkader, Mohamed F. "Integration and evaluation of a video surveillance system." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2817.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Tuvskog, Johanna. "Evaluation of Face Recognition Accuracy in Surveillance Video." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166758.

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Automatic Face Recognition (AFR) can be useful in the forensic field when identifying people in surveillance footage. In AFR systems it is common to use deep neural networks which perform well if the quality of the images keeps a certain level. This is a problem when applying AFR on surveillance data since the quality of those images can be very poor. In this thesis the CNN FaceNet has been used to evaluate how different quality parameters influence the accuracy of the face recognition. The goal is to be able to draw conclusions about how to improve the recognition by using and avoiding certain parameters based on the conditions. Parameters that have been experimented with are angle of the face, image quality, occlusion, colour and lighting. This has been achieved by using datasets with different properties or by alternating the images. The parameters are meant to simulate different situations that can occur in surveillance footage that is difficult for the network to recognise. Three different models have been evaluated with different amount of embeddings and different training data. The results show that the two models trained on the VGGFace2 dataset performs much better than the one trained on CASIA-WebFace. All models performance drops on images with low quality compared to images with high quality because of the training data including mostly high-quality images. In some cases, the recognition results can be improved by applying some alterations in the images. This could be by using one frontal and one profile image when trying to identify a person or occluding parts of the shape of the face if it gets recognized as other persons with similar face shapes. One main improvement would be to extend the training datasets with more low-quality images. To some extent, this could be achieved by different kinds of data augmentation like artificial occlusion and down-sampled images.
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Tanase, Cristina-Madalina. "Multi-person tracking system for complex outdoor environments." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-245082.

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The thesis represents the research in the domain of modern video tracking systems and presents the details of the implementation of such a system. Video surveillance is a high point of interest and it relies on robust systems that interconnect several critical modules: data acquisition, data processing, background modeling, foreground detection and multiple object tracking. The present work analyzes different state of the art methods that are suitable for each module. The emphasis of the thesis is on the background subtraction stage, as the final accuracy and performance of the person tracking dramatically dependent on it. The experimental results show the performance of four different foreground detection algorithms, including two variations of self-organizing feature maps for background modeling, a machine learning technique. The undertaken work provides a comprehensive view of the actual state of the research in the foreground detection field and multiple object tracking and offers solution for common problems that occur when tracking in complex scenes. The chosen data set for experiments covers extremely different and complex scenes (outdoor environments) that allow a detailed study of the appropriate approaches and emphasize the weaknesses and strengths of each algorithm. The proposed system handles problems like: dynamic backgrounds, illumination changes, camouflage, cast shadows, frequent occlusions and crowded scenes. The tracking obtains a maximum Multiple Object Tracking Accuracy of 92,5% for the standard video sequence MWT and a minimum of 32,3% for an extremely difficult sequence that challenges every method.
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Sutor, S. R. (Stephan R. ). "Large-scale high-performance video surveillance." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205618.

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Abstract The last decade was marked by a set of harmful events ranging from economical crises to organized crime, acts of terror and natural catastrophes. This has led to a paradigm transformation concerning security. Millions of surveillance cameras have been deployed, which led to new challenges, as the systems and operations behind those cameras could not cope with the rapid growth in number of video cameras and systems. Looking at today’s control rooms, often hundreds or even thousands of cameras are displayed, overloading security officers with irrelevant information. The purpose of this research was the creation of a novel video surveillance system with automated analysis mechanisms which enable security authorities and their operators to cope with this information flood. By automating the process, video surveillance was transformed into a proactive information system. The progress in technology as well as the ever increasing demand in security have proven to be an enormous driver for security technology research, such as this study. This work shall contribute to the protection of our personal freedom, our lives, our property and our society by aiding the prevention of crime and terrorist attacks that diminish our personal freedom. In this study, design science research methodology was utilized in order to ensure scientific rigor while constructing and evaluating artifacts. The requirements for this research were sought in close cooperation with high-level security authorities and prior research was studied in detail. The created construct, the “Intelligent Video Surveillance System”, is a distributed, highly-scalable software framework, that can function as a basis for any kind of high-performance video surveillance system, from installations focusing on high-availability to flexible cloud-based installation that scale across multiple locations and tens of thousands of cameras. First, in order to provide a strong foundation, a modular, distributed system architecture was created, which was then augmented by a multi-sensor analysis process. Thus, the analysis of data from multiple sources, combining video and other sensors in order to automatically detect critical events, was enabled. Further, an intelligent mobile client, the video surveillance local control, which addressed remote access applications, was created. Finally, a wireless self-contained surveillance system was introduced, a novel smart camera concept that enabled ad hoc and mobile surveillance. The value of the created artifacts was proven by evaluation at two real-world sites: An international airport, which has a large-scale installation with high-security requirements, and a security service provider, offering a multitude of video-based services by operating a video control center with thousands of cameras connected
Tiivistelmä Viime vuosikymmen tunnetaan vahingollisista tapahtumista alkaen talouskriiseistä ja ulottuen järjestelmälliseen rikollisuuteen, terrori-iskuihin ja luonnonkatastrofeihin. Tämä tilanne on muuttanut suhtautumista turvallisuuteen. Miljoonia valvontakameroita on otettu käyttöön, mikä on johtanut uusiin haasteisiin, koska kameroihin liittyvät järjestelmät ja toiminnot eivät pysty toimimaan yhdessä lukuisien uusien videokameroiden ja järjestelmien kanssa. Nykyajan valvontahuoneissa voidaan nähdä satojen tai tuhansien kameroiden tuottavan kuvaa ja samalla runsaasti tarpeetonta informaatiota turvallisuusvirkailijoiden katsottavaksi. Tämän tutkimuksen tarkoitus oli luoda uusi videovalvontajärjestelmä, jossa on automaattiset analyysimekanismit, jotka mahdollistavat turva-alan toimijoiden ja niiden operaattoreiden suoriutuvan informaatiotulvasta. Automaattisen videovalvontaprosessin avulla videovalvonta muokattiin proaktiiviseksi tietojärjestelmäksi. Teknologian kehitys ja kasvanut turvallisuusvaatimus osoittautuivat olevan merkittävä ajuri turvallisuusteknologian tutkimukselle, kuten tämä tutkimus oli. Tämä tutkimus hyödyttää yksittäisen ihmisen henkilökohtaista vapautta, elämää ja omaisuutta sekä yhteisöä estämällä rikoksia ja terroristihyökkäyksiä. Tässä tutkimuksessa suunnittelutiedettä sovellettiin varmistamaan tieteellinen kurinalaisuus, kun artefakteja luotiin ja arvioitiin. Tutkimuksen vaatimukset perustuivat läheiseen yhteistyöhön korkeatasoisten turva-alan viranomaisten kanssa, ja lisäksi aiempi tutkimus analysoitiin yksityiskohtaisesti. Luotu artefakti - ’älykäs videovalvontajärjestelmä’ - on hajautettu, skaalautuva ohjelmistoviitekehys, joka voi toimia perustana monenlaiselle huipputehokkaalle videovalvontajärjestelmälle alkaen toteutuksista, jotka keskittyvät saatavuuteen, ja päättyen joustaviin pilviperustaisiin toteutuksiin, jotka skaalautuvat useisiin sijainteihin ja kymmeniin tuhansiin kameroihin. Järjestelmän tukevaksi perustaksi luotiin hajautettu järjestelmäarkkitehtuuri, jota laajennettiin monisensorianalyysiprosessilla. Siten mahdollistettiin monista lähteistä peräisin olevan datan analysointi, videokuvan ja muiden sensorien datan yhdistäminen ja automaattinen kriittisten tapahtumien tunnistaminen. Lisäksi tässä työssä luotiin älykäs kännykkäsovellus, videovalvonnan paikallinen kontrolloija, joka ohjaa sovelluksen etäkäyttöä. Viimeksi tuotettiin langaton itsenäinen valvontajärjestelmä – uudenlainen älykäs kamerakonsepti – joka mahdollistaa ad hoc -tyyppisen ja mobiilin valvonnan. Luotujen artefaktien arvo voitiin todentaa arvioimalla ne kahdessa reaalimaailman ympäristössä: kansainvälinen lentokenttä, jonka laajamittaisessa toteutuksessa on korkeat turvavaatimukset, ja turvallisuuspalveluntuottaja, joka tarjoaa moninaisia videopohjaisia palveluja videovalvontakeskuksen avulla käyttäen tuhansia kameroita
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Sinn, Richard M. Eng Massachusetts Institute of Technology. "Virtual pan-tilt-zoom for a wide-area-video surveillance system." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46489.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 95).
Advancements in the CMOS Image Sensor have enabled very high-performance, high resolution imaging systems to be built at relatively low cost. The availability of high-pixel count video imaging systems that can cover a wide field-of-view enables a surveillance technique called Virtual Pan-Tilt-Zoom. Virtual Pan-Tilt-Zoom provides the same functional properties as a mechanical pan-tilt-zoom setup, but it does not suffer from the physical limitations presented by a mechanical setup. A video system using Virtual Pan-Tilt-Zoom would have immediate continuous access to a high pixel-count image representing a wide coverage area, and it would enable a user to "virtually" pan, tilt, and zoom around the coverage area by reading out only the relevant image data associated with a Region of Interest that is dynamically defined by the user. This paper will examine the various camera electronics readout architectures that are possible to support the Virtual Pan-Tilt-Zoom function. Then, this project will examine and implement a specific implementation of the readout architecture for a high-resolution video camera system developed at MIT Lincoln Laboratory. The Multi-Aperture Sparse Imager Video System (MASIV) developed at MIT Lincoln Laboratory incorporates CMOS imagers to create an 880 Mega pixel image, and was used as the platform to implement the camera electronics for Virtual Pan-Tilt-Zoom functionality.
by Richard Sinn.
M.Eng.
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Zhao, Wei. "Digital Surveillance Based on Video CODEC System-on-a-Chip (SoC) Platforms." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/334.

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Today, most conventional surveillance networks are based on analog system, which has a lot of constraints like manpower and high-bandwidth requirements. It becomes the barrier for today’s surveillance network development. This dissertation describes a digital surveillance network architecture based on the H.264 coding/decoding (CODEC) System-on-a-Chip (SoC) platform. The proposed digital surveillance network architecture includes three major layers: software layer, hardware layer, and the network layer. The following outlines the contributions to the proposed digital surveillance network architecture. (1) We implement an object recognition system and an object categorization system on the software layer by applying several Digital Image Processing (DIP) algorithms. (2) For better compression ratio and higher video quality transfer, we implement two new modules on the hardware layer of the H.264 CODEC core, i.e., the background elimination module and the Directional Discrete Cosine Transform (DDCT) module. (3) Furthermore, we introduce a Digital Signal Processor (DSP) sub-system on the main bus of H.264 SoC platforms as the major hardware support system for our software architecture. Thus we combine the software and hardware platforms to be an intelligent surveillance node. Lab results show that the proposed surveillance node can dramatically save the network resources like bandwidth and storage capacity.
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Books on the topic "Video surveillance system"

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IBM System Storage N series and digital video surveillance. [United States?]: IBM, International Technical Support Organization, 2008.

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Osuna, Alex. IBM System Storage N series and digital video surveillance. [United States?]: IBM, International Technical Support Organization, 2008.

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Dufour, Jean-Yves, ed. Intelligent Video Surveillance Systems. Hoboken, NJ USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118577851.

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Remagnino, Paolo, Graeme A. Jones, Nikos Paragios, and Carlo S. Regazzoni, eds. Video-Based Surveillance Systems. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4.

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Dufour, Jean-Yves. Intelligent video surveillance systems. London: ISTE, 2013.

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S, Regazzoni Carlo, Fabri Gianni, Vernazza Gianni, and Workshop on Advanced Video Based Surveillance (1998 : Genoa, Italy), eds. Advanced video-based surveillance systems. Boston: Kluwer Academic Publishers, 1999.

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Regazzoni, Carlo S., Gianni Fabri, and Gianni Vernazza, eds. Advanced Video-Based Surveillance Systems. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5085-3.

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Foresti, Gian Luca, Petri Mähönen, and Carlo S. Regazzoni, eds. Multimedia Video-Based Surveillance Systems. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5.

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Regazzoni, Carlo S. Advanced Video-Based Surveillance Systems. Boston, MA: Springer US, 1999.

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A, Velastin Sergio, Remagnino Paolo 1963-, and Institution of Electrical Engineers, eds. Intelligent distributed video surveillance systems. London: Institution of Electrical Engineers, 2006.

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Book chapters on the topic "Video surveillance system"

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Durak, Nurcan, Adnan Yazici, and Roy George. "Online Surveillance Video Archive System." In Lecture Notes in Computer Science, 376–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-69423-6_37.

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Venkatesan, R., P. Dinesh Anton Raja, and A. Balaji Ganesh. "Video Surveillance Based Tracking System." In Advances in Intelligent Systems and Computing, 369–78. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2126-5_41.

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Rajesh, T. M., and Kavyashree Dalawai. "Automated Video Surveillance System Using Video Analytics." In Intelligent Computing and Communication, 451–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_43.

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Cucchiara, R., C. Grana, G. Neri, M. Piccardi, and A. Prati. "The Sakbot System for Moving Object Detection and Tracking." In Video-Based Surveillance Systems, 145–57. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_12.

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Chang, Ting-Hsun, and Shaogang Gong. "Bayesian Modality Fusion for Tracking Multiple People with a Multi-Camera System." In Video-Based Surveillance Systems, 79–87. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_6.

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Siebel, N. T., and S. J. Maybank. "On the Use of Colour Filtering in an Integrated Real-Time People Tracking System." In Video-Based Surveillance Systems, 167–75. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_14.

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Regazzoni, Carlo S., Claudio Sacchi, and Gianluca Gera. "Intelligence Distribution of a Third Generation People Counting System Transmitting Information Over an Urban Digital Radio Link." In Video-Based Surveillance Systems, 251–65. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_21.

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Raggio, Marco, Ivano Barbieri, Alberto Cabitto, and Luís Corte-Real. "Scalable H.324 Video-Based Surveillance System." In Multimedia Video-Based Surveillance Systems, 162–72. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_14.

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Senior, Andrew. "Privacy Protection in a Video Surveillance System." In Protecting Privacy in Video Surveillance, 35–47. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-301-3_3.

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Collins, Robert T., Omead Amidi, and Takeo Kanade. "Acquiring Multi-View Video with an Active Camera System." In Multisensor Surveillance Systems, 135–47. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0371-2_8.

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Conference papers on the topic "Video surveillance system"

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Xu, Zhengya, and Hong Ren Wu. "Smart video surveillance system." In 2010 IEEE International Conference on Industrial Technology. IEEE, 2010. http://dx.doi.org/10.1109/icit.2010.5472694.

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Banu, Virgil Claudiu, Ilona Madalina Costea, Florin Codrut Nemtanu, and Iulian Badescu. "Intelligent video surveillance system." In 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2017. http://dx.doi.org/10.1109/siitme.2017.8259891.

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Krishna, Arppitha, Neha Pendkar, Shruti Kasar, Umesh Mahind, and Shridhar Desai. "Advanced Video Surveillance System." In 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE, 2021. http://dx.doi.org/10.1109/icspc51351.2021.9451694.

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Huang, Hu, Benchang Zheng, Kai Cui, Dongdong Wang, Zhihao Wu, and Jia Zhang. "Passive video surveillance system." In International Symposium on Artificial Intelligence and Robotics 2021, edited by Shota Nakashima, Shenglin Mu, and Huimin Lu. SPIE, 2021. http://dx.doi.org/10.1117/12.2607203.

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Gao, Siyuan. "An Intelligent Video Surveillance System." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5661050.

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Khelvas, Alexander, Darya Demyanova, Alexander Gilya-Zetinov, Egor Konyagin, Roman Khafizov, and Ruslan Pashkov. "Adaptive distributed video surveillance system." In 2020 International Conference on Technology and Entrepreneurship - Virtual (ICTE-V). IEEE, 2020. http://dx.doi.org/10.1109/icte-v50708.2020.9113774.

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Hsu, Charles, and Harold Szu. "Smart sensing surveillance video system." In SPIE Commercial + Scientific Sensing and Imaging, edited by Liyi Dai, Yufeng Zheng, Henry Chu, and Anke D. Meyer-Bäse. SPIE, 2016. http://dx.doi.org/10.1117/12.2239982.

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Yu, Chen, Jianan Wang, Jiayuan Shan, and Ming Xin. "Multi-UAV UWA video surveillance system." In 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2016. http://dx.doi.org/10.1109/icarcv.2016.7838810.

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Fang Li, Maylor K. H. Leung, Mehul Mangalvedhekar, and Mark Balakrishnan. "Automated video surveillance and alarm system." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620578.

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Momin, B. F., and Y. R. Jere. "Mining visitors in Video Surveillance system." In 2015 International Conference on Innovations in Information,Embedded and Communication Systems (ICIIECS). IEEE, 2015. http://dx.doi.org/10.1109/iciiecs.2015.7193040.

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Reports on the topic "Video surveillance system"

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Jain, Ramesh. Multiple Perspective Interactive Video Surveillance and Monitoring System. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada379758.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Abstract:
Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
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