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Journal articles on the topic 'Video surveillance system'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

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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2

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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3

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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5

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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6

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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9

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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10

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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11

Kardas, Karani, and Nihan Kesim Cicekli. "SVAS: Surveillance Video Analysis System." Expert Systems with Applications 89 (December 2017): 343–61. http://dx.doi.org/10.1016/j.eswa.2017.07.051.

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12

Abdulhamid, Mohanad, and Mwongeera Murungi. "System design of video surveillance." System research and information technologies, no. 4 (December 23, 2019): 49–58. http://dx.doi.org/10.20535/srit.2308-8893.2019.4.05.

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13

Ding, Xue Feng. "Based on Computer Substation Monitoring Controlled Video System." Applied Mechanics and Materials 203 (October 2012): 171–74. http://dx.doi.org/10.4028/www.scientific.net/amm.203.171.

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In order to remote video surveillance is a necessary means of Substation, this describes the composition of remote video surveillance system, video surveillance systems, transmission systems, substation remote video surveillance and the linkage of the work process of the alarm, the unmanned subhastation should have the functionality and remote video monitoring of communication conditions. The remote monitoring system for data signals as the data source through the computer to control the substation video surveillance system, the substation as a conference at the venue, set up the meeting subsystem, a wider range of monitoring of the substation.

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14

Zhou, Lei, Wei Qi Yan, Yun Shu, and Jian Yu. "CVSS." International Journal of Digital Crime and Forensics 10, no. 1 (January 2018): 79–91. http://dx.doi.org/10.4018/ijdcf.2018010107.

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A large amount of surveillance videos and images need sufficient storage. In this article, an architecture of cloud-based surveillance systems and its modules will be designed, the Cloud-based Visual Surveillance System (CVSS) will be implemented on a private cloud using a Virtual Machine (VM). The users are able to link their cameras to the CVSS system so that the goal of this design can be achieved. The authors' CVSS system is able to push notification messages of captured videos to receivers, and their users could receive a surveillance video along with its events. The CVSS system fully makes use of the merits of cloud computing, which make it more advanced as stated in the evaluation section of this article. The contributions of this article are to be implemented in the CVSS system with: (1) video stream input, (2) intelligent visual surveillance, (3) real-time video transcoding and storage, (4) message pushing and media streaming output.

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Li, Hao, Tianhao Xiezhang, Cheng Yang, Lianbing Deng, and Peng Yi. "Secure Video Surveillance Framework in Smart City." Sensors 21, no. 13 (June 28, 2021): 4419. http://dx.doi.org/10.3390/s21134419.

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In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

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16

Nguyen, Cuong, Wu-chi Feng, and Feng Liu. "Hotspot: Making computer vision more effective for human video surveillance." Information Visualization 15, no. 4 (July 25, 2016): 273–85. http://dx.doi.org/10.1177/1473871616630015.

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Studies have shown that the human capability of monitoring multiple surveillance videos is limited. Computer vision techniques have been developed to detect abnormal events to support human video surveillance; however, their results are often unreliable, thus distracting surveillance operators and making them miss important events. This article presents Hotspot as a surveillance video visualization system that can effectively leverage noisy computer vision techniques to support human video surveillance. Hotspot consists of two views: a designated focus view to summarize videos with detected events and a video-bank view surrounding the focus view to display source surveillance videos. The focus view allows an operator to quickly dismiss false alarms and focus on true alarms. The video-bank view allows for extended human video analysis after an important event is detected. Hotspot further provides visual links to assist quick attention switch from the focus view to the video-bank view. Our experiments show that Hotspot can effectively integrate noisy, automatic computer vision detection results and better support human video surveillance tasks than the baseline video surveillance with no or only basic computer vision support.

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17

Han, Wei. "Design of Wireless Video Surveillance System." Applied Mechanics and Materials 321-324 (June 2013): 961–64. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.961.

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Wireless video surveillance is of increasing importance. Many wireless video surveillance systems require a well optimized video codec for low-power operations. Compression of the captured video is normally needed in order to reduce the amount of video data to be delivered as well as to increase the video storage capability of the surveillance system. In this paper, a method which can greatly reduce the complexity for one of the most time-consuming parts in a video encoder was proposed.

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18

Ponnapalli, Bharath, Gangadhara Sai Kutukuppala, Sandeep Monavarthi, Sri Vachan Goli, and B. Rajesh. "Monitoring of Video Surveillance." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 983–88. http://dx.doi.org/10.22214/ijraset.2023.50212.

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Abstract: The system gathers a sizable dataset of human faces during the data gathering phase in order to train the machine learning algorithms. In the face detection phase, computer vision algorithms are used to discover and recognise human faces in an image or video stream. You may accomplish this by using The deep convolutional neural network (CNN) architecture of the VGG16 algorithm is one of the most widely used algorithms for this job. The VGG16 algorithm has produced cutting-edge outcomes in a variety of computer vision applications, such as face recognition. Computer vision and machine learning algorithms that can detect and validate human faces. Access control, monitoring, and security systems are just a few examples of the many uses for the system. Data gathering, face detection, face recognition, and verification are a few of the processes that the project goes through. A personal identification method called face recognition analyzes a person's physical features to determine their identity to detect and extract facial features from the Image. The process for recognizing faces in humans consists of two phases: face detection, which occurs quickly in people unless the face is nearby, and introduction, which identifies faces as belonging to specific people

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19

Vasylenko, Mykola, and Maksym Haida. "Video Surveillance System of Target Contour." Electronics and Control Systems 1, no. 67 (May 12, 2021): 9–14. http://dx.doi.org/10.18372/1990-5548.67.15556.

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The paper considers the problem of object detection and recognition, namely, its solution without the use of expensive, resource-intensive and complex data collection and processing systems, with the possibility of its mobility, ease of installation and initial setup. The available ways and methods of solving the problem, their advantages and disadvantages are given. The operation of the system according to an algorithm developed on the basis of the method of object recognition, namely the selection of contours by a filter based on the Canny operator, is presented. The article presents intermediate and final illustrations of the system algorithm at each step, which gives an opportunity to get acquainted with its advantages over classical video surveillance systems and some disadvantages. Elements such as a webcam with a video frame rate of 25 frames per second, a mobile phone and a personal computer with the MatLab2021 programming environment installed (chosen due to the ease of use and the presence of built-in image processing functions) were used to demonstrate the system's performance.

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20

Mohammadi, Reza, and Reza Javidan. "An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony." International Journal of Intelligent Information Technologies 12, no. 4 (October 2016): 45–62. http://dx.doi.org/10.4018/ijiit.2016100103.

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In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

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21

Islam, Muhammad, Abdulsalam S. Dukyil, Saleh Alyahya, and Shabana Habib. "An IoT Enable Anomaly Detection System for Smart City Surveillance." Sensors 23, no. 4 (February 20, 2023): 2358. http://dx.doi.org/10.3390/s23042358.

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Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.

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22

Zhu, Tao, Zhi Guo Du, and Wei Jun Hong. "Effect Evaluation Method of Video Surveillance System." Applied Mechanics and Materials 651-653 (September 2014): 2301–5. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2301.

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According to the problem of over-subjective in current effect evaluation method of video surveillance system, a comprehensive effect evaluation index system of video surveillance system is established. Hierarchy belongingness between multiple index elements is established, the function of each index elements is quantitatively analyzed with the help of MATLAB, and the effect evaluation of video surveillance system is performed with the application of fuzzy comprehensive evaluation method. It is proved in instances that the effect evaluation method of video surveillance system with the application of the index system is capable of evaluating the video surveillance system effectively and quantitatively.

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23

A.S, Vidya, and V. K. Govindan. "Video Surveillance System for Security Applications." International Journal of Computer Applications 74, no. 1 (July 26, 2013): 17–24. http://dx.doi.org/10.5120/12849-9294.

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24

Wasudeo P., Mr Rahane. "VIDEO SURVEILLANCE BASED TRAFFIC MONITORING SYSTEM." IOSR Journal of Engineering 02, no. 04 (April 2012): 538–43. http://dx.doi.org/10.9790/3021-0204538543.

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25

S.TamilSelvan, S. TamilSelvan. "Secure Authentication System Using Video Surveillance." IOSR Journal of Computer Engineering 11, no. 6 (2013): 97–104. http://dx.doi.org/10.9790/0661-11697104.

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26

Gao, Yunhui, Xiangfeng Suo, and Yan Li. "Embedded Mobile Video Surveillance Service System." Journal of Physics: Conference Series 1992, no. 2 (August 1, 2021): 022114. http://dx.doi.org/10.1088/1742-6596/1992/2/022114.

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27

Sapkal, Ajinkya. "Human Detection in Video Surveillance System." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2171–73. http://dx.doi.org/10.17762/ijritcc2321-8169.150488.

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28

Praveen Kumar K, Shahina S, Mrudula K P, Mohamed Harish, and Anu T P. "Video Surveillance System and Violence Detection." IRO Journal on Sustainable Wireless Systems 5, no. 2 (June 2023): 138–49. http://dx.doi.org/10.36548/jsws.2023.2.005.

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The Video Surveillance System is an enhanced version of modern CCTV surveillance. The system is trained using Yolo algorithm to detect the weapons and also is trained using Convolutional Neural Network (CNN) to detect Violence in public places. The system not only detects but also it sends notification to the central control of the system like Police Stations and the police will be able to take necessary actions. The users can also send complaints and use the android application in case of emergency to alert the police if the user is in danger situations. The police will be able to take quick actions using the locations available with the complaint or emergency. By combining CCTV surveillance and weapon detection the system can act as a Central Surveillance System. The system will not record the full video instead take the snapshot of the detected weapons or violence and send it to the admin saving the memory.

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Raj, L. Arun, and C. Thangapandian. "Motion Based Summarization and Grouping of Events for Video Surveillance System." International Journal of Engineering Research 3, no. 4 (April 1, 2014): 258–60. http://dx.doi.org/10.17950/ijer/v3s4/417.

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Bello, Oladipupo. "EMPIRICAL INVESTIGATION OF VIDEO SURVEILLANCE SYSTEM ADOPTION IN NIGERIA." International Journal of Engineering Applied Sciences and Technology 6, no. 8 (December 1, 2021): 12–19. http://dx.doi.org/10.33564/ijeast.2021.v06i08.002.

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—The acceptance of video surveillance systems at homes, offices and business enterprises nowadays to reduce incidences of theft, burglaries, sexual offences and other forms of crimes is increasing. This study investigated the factors that influence adoption of video surveillance systems in Nigeria. Based on Rogers’ diffusion of innovation model, the effect of relative advantage, compatibility, complexity, trialability and observability on adoption of video surveillance systems was examined. Structural equation model analysis of the collected data shows that relative advantage, compatibility, trialability and observability significantly influence adoption of video surveillance systems. However, the results indicate that adoption of video surveillance systems is not influenced significantly by complexity. Thus, complexity of video surveillance systems is perceived as a barrier to adoption of this technological innovation among Nigerians.

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Chlenov, A. N., A. V. Kochegarov, T. A. Butcinskaya, and A. M. Aleshkov. "Application of video surveillance equipment for fire detection." Technology of technosphere safety 91 (2021): 107–20. http://dx.doi.org/10.25257/tts.2021.1.91.107-120.

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Introduction. The fire safety of the protected object is determined by the possibility of reliable fire detection. Insufficiently effective operation of the fire alarm system can lead to the death of people, unacceptable material and other damage. The video surveillance system, being one of the main links of the engineering security system, monitors emergency situations in real time and, in particular, a breakout of fire at the protected object. Goals and objectives. The purpose of the article is to analyze the state and trends in the development of video surveillance equipment aimed at improving the fire safety of protected objects. Tasks include regulatory justification of the use of video surveillance in fire alarm systems, review of methods of fire detection by video surveillance and improving their effectiveness under the influence of interfering factors, as well as evaluation of the effectiveness of video surveillance in fire protection systems. Methods. The methods of the system analysis and probability theory are used. Results and discussion. The requirements of regulatory documents for detectors with a video detection channel for their effective operation in fire alarm systems are considered. The main directions of development of means and methods of application of video surveillance in fire protection systems are defined. It is shown that reducing the risk of non-detection of fire to an acceptable level can be achieved by increasing the probability of effective operation of the fire alarm system, as well as by increasing the positive impact of the video surveillance system, which is a part of the complex security system of the object. Conclusions. Video surveillance significantly increases the efficiency of fire detection. It allows you to get a visual picture of the state of the protected object, which has such a high information content that no other security means can provide. The use of video surveillance equipment in fire alarm systems, as well as their integration with industrial and fire automation systems is one of the main reserves for increasing the fire safety of facilities. Keywords: video surveillance, fire detector with video detection channel, fire alarm, video analytics, fire protection

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Li, Yang, Qing Hong Wu, and Xue Xiao. "Based Embedded System Applications in Intelligent Home Remote Monitoring." Applied Mechanics and Materials 602-605 (August 2014): 2317–20. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2317.

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With the continuous improvement of security awareness, home security has become the focus of attention. The actual demand for home video surveillance system, designed a cheap, practical, small size and low power consumption of video surveillance systems, this paper uses microprocessor S3C2440 ARM9 core as the core hardware control, embedded Linux operating system with software the control core, and cheap, generic USB camera video capture device as a front end to complete the design of a home video surveillance system.

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Xiong, Yong-Hua, Shao-Yun Wan, Yong He, and Dan Su. "Design and Implementation of a Prototype Cloud Video Surveillance System." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 1 (January 20, 2014): 40–47. http://dx.doi.org/10.20965/jaciii.2014.p0040.

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Cloud-based video surveillance systems, as a new cloud computing service model, are an emerging research topic, both at home and abroad. Current research is mainly focused on exploring applications of the system. This paper proposes a design and implementation method for cloud-based video surveillance systems using the characteristics of cloud computing, such as parallel computing, large storage space, and easy expandability. The system architecture and function modules are built, and a prototype cloud-based video surveillance system is established in a campus network using key technologies, including virtual machine task access control, video-data distributed storage, and database-active communicationmethods. Using the system, the user is able to place a webcam in a location that requires monitoring so that video surveillance can be achieved, and video data can be viewed through a browser. The system has the following advantages: low investment and maintenance cost, high portability, easily extendable, superior data security, and excellent sharing. As a private cloud server in the campus network, the system is able to not only provide convenient video surveillance services, but it can also be an excellent practical experimental platform for cloud computing-related research, which carries outstanding application value.

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Przybylo, Jaromir, Joanna Grabska-Chrzastowska, and Przemyslaw Korohoda. "Low-Cost Scalable Home Video Surveillance System." Image Processing & Communications 19, no. 2-3 (September 1, 2014): 51–58. http://dx.doi.org/10.1515/ipc-2015-0010.

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Abstract Automated and intelligent video processing and analysis systems are becoming increasingly popular in video surveillance. Such systems must meet a number of requirements, such as threat detection and real-time video recording. Furthermore, they cannot be expensive and must not consume too much energy because they have to operate continuously. The work presented here focuses on building a home video surveillance system matching the household budget and possibly making use of hardware available in the house. Also, it must provide basic functionality (such as video recording and detecting threats) all the time, and allow for a more in-depth analysis when more computing power be available.

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Nam, Yunyoung, Seungmin Rho, and Jong Hyuk Park. "Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata." Multimedia Tools and Applications 57, no. 2 (December 22, 2010): 315–34. http://dx.doi.org/10.1007/s11042-010-0677-x.

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Vishaka Gayathri, D., Shrutee Shree, Taru Jain, and K. Sornalakshmi. "Real Time System for Human Identification and Tracking from Surveillance Videos." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 244. http://dx.doi.org/10.14419/ijet.v7i3.12.16034.

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The need for intelligent surveillance systems has raised the concerns of security. A viable system with automated methods for person identification to detect, track and recognize persons in real time is required. The traditional detection techniques have not been able to analyze such a huge amount of live video generated in real-time. So, there is a necessity for live streaming video analytics which includes processing and analyzing large scale visual data such as images or videos to find content that are useful for interpretation. In this work, an automated surveillance system for real-time detection, recognition and tracking of persons in video streams from multiple video inputs is presented. In addition, the current location of an individual can be searched with the tool bar provided. A model is proposed, which uses a messaging queue to receive/transfer video feeds and the frames in the video are analyzed using image processing modules to identify and recognize the person with respect to the training data sets. The main aim of this project is to overcome the challenges faced in integrating the open source tools that build up the system for tagging and searching people.

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37

Lys, Ruslan, and Yurii Opotyak. "Development of a Video Surveillance System for Motion Detection and Object Recognition." Advances in Cyber-Physical Systems 8, no. 1 (May 10, 2023): 50–56. http://dx.doi.org/10.23939/acps2023.01.050.

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This article explores the development of a video surveillance system that utilizes cuttingedge technology to analyze the video stream in real-time, identify motion, and recognize objects within the video stream. The functionality of this system enables it to provide a high level of accuracy in identifying objects, even in low-light conditions or with low-resolution cameras. The software system has been designed as a user-friendly desktop application with the latest technologies and features that will ensure its relevance and easy maintenance in the future. To ensure that the developed desktop application meets common optimization requirements, extensive testing has been conducted to evaluate its resource usage. The resulting system is an efficient and reliable tool for monitoring and detecting movement in various locations, providing enhanced security measures and public safety.

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Ghosh, Gopal, Kavita, Monica Sood, and Sahil Verma. "Internet of Things Based Video Surveillance Systems for Security Applications." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2582–88. http://dx.doi.org/10.1166/jctn.2020.8933.

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The principle point of this paper is to talk about the Video surveillance system plays major role in the security applications now a days. The need for surveillance systems are increasing drastically. Internet of things makes Video surveillance more effective. Internet of Things is an interrelated communication network for several systems and having the ability to transfer the information through the network without human or computer interaction. This paper reviews the network traffic due to Video surveillance and its management with Software Defined Networking. This paper also reviews the applications of Internet of things in Video surveillance system.

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39

Kachhava, Rajendra, Vivek Srivastava, Rajkumar Jain, and Ekta Chaturvedi. "Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras." Advanced Materials Research 403-408 (November 2011): 4968–73. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4968.

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In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

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Przybylo, Jaromir. "Object detection and tracking for low-cost video surveillance system." Image Processing & Communications 18, no. 2-3 (December 1, 2013): 91–99. http://dx.doi.org/10.2478/v10248-012-0083-2.

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Abstract Automated and intelligent video surveillance systems play important role in the modern world. Since the amount of various video streams that must be analyzed grows, such artificial intelligence systems can assist humans in performing tiresome tasks. As a result, the effectiveness of response to a dangerous situations is increasing (detect unexpected movement or unusual behavior that may pose a threat to people, property and infrastructure). Video surveillance systems have to meet several requirements: must be accurate and not produce too many false alarms, moreover it must be able to process the received video stream in real-time to provide a sufficient response time. The work presented here focuses on the selected challenges of scene analysis in video surveillance systems (object detection/tracking, effectiveness of the whole system). The aim of the research is to design a low-budget surveillance system, that can be used for example in a home security monitoring. Such solution can be use not only to surveillance but also to monitor elderly person at home or provide new ways of interacting in human-computer interaction systems.

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Фахми, Ш. С., С. А. Селиверстов, Е. В. Костикова, Р. Р. Муксимова, and В. О. Титов. "Real-time marine vessel surveillance video system." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 2(52) (June 5, 2021): 98–105. http://dx.doi.org/10.37220/mit.2021.52.2.015.

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Анализируется процесс развития систем наблюдения. Раскрываются особенности технологических изменений систем наблюдения 1-го, 2-го и 3-го поколений. Декларируется, что современные полупроводниковые технологии позволяют перейти к более развитым системам видеонаблюдения 3-го поколения, где преобразование и обработка видеоинформации выполняются непосредственно в видеодатчиках на этапе формирования кадров. Умные камеры расширяют функциональность видеосенсора 3-го поколения, обеспечивая бортовую высокоуровневую обработку видео. Рассмотрены эволюция систем наблюдения и архитектура обработки видеоинформации с использованием интеллектуальных видеокамер с высоким динамическим диапазоном. Представлена графическая интерпретация, иллюстрирующая процесс эволюции систем видеонаблюдения от 1-го к 3-му поколению. Проанализированы функции современных систем видеонаблюдения и переход от высокоуровневой обработки видео из систем общего назначения во встраиваемые системы. Рассмотрен состав видеосистемы наблюдения с использованием интеллектуальной видеокамеры, включающий видеодатчик, блок обработки и блок управления связи. Описаны условия в которых морские системы видеонаблюдения используются. Приведены результаты экспериментальных исследований и выполнены оценки производительности. Показаны достигнутые результаты производительности для различных реализаций алгоритма обнаружения морских судов и необходимое время выполнения при обработке одного изображения с полным разрешением на стандартном настольном компьютере Pentium 4 с частотой 2,4 ГГц. с использованием реконфигурируемой системой на кристалле. The process of development of observation systems is analyzed. The features of technological changes in observation systems of the 1st, 2nd and 3rd generations are revealed. It is declared that modern semiconductor technologies make it possible to move to more advanced third-generation video surveillance systems, where the conversion and processing of video information is performed directly in video sensors at the stage of framing. Smart cameras extend the functionality of the 3rd generation image sensor to provide on-board high-level video processing. The evolution of surveillance systems and architecture of video information processing using smart cameras with a high dynamic range are considered. A graphical interpretation is presented that illustrates the evolution of video surveillance systems from the 1st to the 3rd generation. The functions of modern video surveillance systems and the transition from high-level video processing from general-purpose systems to embedded systems are analyzed. The composition of a video surveillance system using an intelligent camera is considered, including a video sensor, a processing unit and a communication control unit. The conditions in which marine video surveillance systems are used are described. The results of experimental studies are presented and performance estimates are performed. Shown are the achieved performance results for various implementations of the ship detection algorithm and the required execution time when processing one full resolution image on a standard Pentium 4 desktop computer running at 2.4 GHz. using a reconfigurable system on a chip.

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Cao, Han, Hao Zeng, and Yang Fu. "Research and Implementation of RTP/RTCP in Mobile Video Surveillance System." Advanced Materials Research 926-930 (May 2014): 2521–24. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2521.

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As people for the need on more security, traditional fixed video surveillance cannot satisfied with people’s varying requirements. This paper demonstrates the framework of a set of mobile video surveillance which support CDMA2000/TD-SCDMA/WCDMA network, RTP protocol is chosen as the transport protocol and H.264 is chosen as the video compression standard. Select RTCP protocol gather packet loss fraction parameter that controls the transmission rate of RTP traffic. We analyze RTP/RTCP protocol and media data transmission process in video surveillance system; discuss the current video transmission method in video surveillance system, the problem of packet loss while transporting video data, and extract packet process of RTP packet.

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Cheng, Wan Sheng, Zhen He Fu, and Shu Xia Leng. "Implementation of Remote Monitoring System Based on S3C2440." Applied Mechanics and Materials 623 (August 2014): 278–82. http://dx.doi.org/10.4028/www.scientific.net/amm.623.278.

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To achieve a monitoring system indoor, design a network video surveillance system which based ARM9 processor. The system uses the S3C2440,which produced by Samsung Corporation as the processing chip of the network video surveillance systems, and the USB camera as a video capture device, connecting to the Internet via a wired way. This paper describes an embedded operating system migration and programming, as well as MJPG-steamer video server software compiler and transplantation in detail. Experimental results show that the system is stable, while the video is clear and smooth.

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Makovoz, K. O. "Intrusion detection methods in cloud video surveillance systems." CTE Workshop Proceedings 1 (March 21, 2013): 53. http://dx.doi.org/10.55056/cte.87.

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Video surveillance systems are a set of equipment and software that is used to organize video surveillance at sites, regardless of their territorial location.Cloud infrastructure can be effectively used to scale a video surveillance system in the following dimensions:- storing video and video analytics metadata;- connection of new surveillance objects (e.g., retail outlets);- implementation of new functions of metadata analysis and archive search;-servicing a large number of users.

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Golda, Thomas, Deborah Guaia, and Verena Wagner-Hartl. "Perception of Risks and Usefulness of Smart Video Surveillance Systems." Applied Sciences 12, no. 20 (October 16, 2022): 10435. http://dx.doi.org/10.3390/app122010435.

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The number of video cameras in public places increases due to different reasons such as detecting dangers (e.g., thefts, robberies, terrorist attacks) and security breaches in crowds. The application of video surveillance systems is sometimes evaluated ambivalently; therefore, the presented study focuses on factors influencing the acceptance of a privacy-friendly, smart video surveillance system. Overall, 216 persons aged between 18 and 81 years participated in an online survey. In terms of the perceived usefulness, there are significant interactions of public spaces × gender and public spaces × time of day. In addition, the assessment of different privacy levels of a video surveillance system differ significantly in terms of perceived risk. Interestingly, men rate the risk concerning their own privacy significantly higher than women do. Participants rate the presented system as fairly useful and slightly risky for their own privacy. The findings of the presented exploratory study provide insight into how people perceive smart video surveillance. These findings have the potential to support the conditions of the use of smart video surveillance systems and to address the possibly affected individuals. smart video surveillance; intelligent video surveillance; machine learning; human pose estimation; privacy protection; online survey; exploratory study; time of day; public areas; gender; perceived privacy risk

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Zhu, Tao, and Wei Jun Hong. "Effect Evaluation of Video Surveillance System on the Basis of Principal Component Analysis." Applied Mechanics and Materials 713-715 (January 2015): 479–81. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.479.

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The effect evaluation of video surveillance system is important for the effect of expected protection on the system. A comprehensive effect evaluation index system of video surveillance system is established. The Principal Component Analysis (PCA) method is applied on the established index system to obtain a new evaluation index system. It is proved in instances that the effect evaluation method of video surveillance system with the application of the index system is capable of evaluating the video surveillance system effectively and quantitatively. The protective effect of the video surveillance system is evaluated objectively on the basis of the new index system with the PCA.

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Oves García, R., L. Valentín, S. A. Serrano, M. A. Palacios-Alonso, and L. Enrique Sucar. "GEOVISUALIZATION FOR SMART VIDEO SURVEILLANCE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W3 (September 25, 2017): 37–44. http://dx.doi.org/10.5194/isprs-annals-iv-4-w3-37-2017.

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Nowadays with the emergence of smart cities and the creation of new sensors capable to connect to the network, it is not only possible to monitor the entire infrastructure of a city, including roads, bridges, rail/subways, airports, communications, water, power, but also to optimize its resources, plan its preventive maintenance and monitor security aspects while maximizing services for its citizens. In particular, the security aspect is one of the most important issues due to the need to ensure the safety of people. However, if we want to have a good security system, it is necessary to take into account the way that we are going to present the information. In order to show the amount of information generated by sensing devices in real time in an understandable way, several visualization techniques are proposed for both local (involves sensing devices in a separated way) and global visualization (involves sensing devices as a whole). Taking into consideration that the information is produced and transmitted from a geographic location, the integration of a Geographic Information System to manage and visualize the behavior of data becomes very relevant. With the purpose of facilitating the decision-making process in a security system, we have integrated the visualization techniques and the Geographic Information System to produce a smart security system, based on a cloud computing architecture, to show relevant information about a set of monitored areas with video cameras.

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Mohammad, Duaa, Inad Aljarrah, and Moath Jarrah. "Searching surveillance video contents using convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (April 1, 2021): 1656. http://dx.doi.org/10.11591/ijece.v11i2.pp1656-1665.

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Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.

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Jiang, Feng. "Target Detection Alarm System Based on Video Surveillance System." Academic Journal of Science and Technology 1, no. 1 (March 3, 2022): 14–19. http://dx.doi.org/10.54097/ajst.v1i1.240.

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Aiming at the problems of heavy video workload and low efficiency of staff in the video monitoring system, a target detection system model combining camera and virtual electronic fence is established, and a virtual electronic fence alarm system with low equipment cost and higher safety factor is developed. The system can set any geometric area independently, recognize the human posture of pedestrians entering the area through the video information collected by the monitoring lens, detect pedestrian intrusion and give an alarm, which plays the role of shock and warning. On this basis, Open Pose algorithm is proposed to further improve the accuracy and rapidity of the system. Experimental results show that this method is feasible and effective.

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Lin, Mao, Yong Wang, and Gang Shi. "Social Security Video Surveillance Systems." Advanced Materials Research 433-440 (January 2012): 5906–10. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5906.

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the reform and open policy has brought us Chinese people rich and colorful. However, the disparity between the rich and poor gap are increasing due to the uneven development in Chinese economic so that state of social security become more and more terrible than the pass. Thus, it is very meaningful to establish a social security video surveillance system to achieve security linkage management by this way that monitors the important insecure point by installing one or more camera to capturing the situation real-time. When there is something happen, the monitoring center will cause an alert signal to administer for further processing. In this paper, we present a scheme to achieve the social security requirement.

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