Design and implementation of real time security surveillance system using IoT

Jyothi, S N; Vardhan, K. Vijaya

doi:10.1109/cesys.2016.7890003

Cited by 41 publications

(15 citation statements)

References 7 publications

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“…The Network within the PNN is split into subnets because of this network isn't fully connected. SOM (Self-Organizing Map Neural Network) [29] have the property of topological preservation is a man-made neural network employed in face recognition. After the feature extraction of facial images, the RBFN or FFNN are alternative popular NN classifiers which accomplish the facial recognition task in easier and accurate way.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Surveillance Through Face Recognition Using HOG and Feedforward Neural Networks

et al. 2019

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Real-time security requirements continue to increase due to the occurrence of various suspicious activities in open and closed environments. Day-today security threats may seriously affect everyone lives. Many techniques have been introduced in this regard, but still some issues remain unaddressed. The work presented in this paper provides video surveillance with improved accuracy and less computational complexity. The most significant part of the system consists of face localization, detection and recognition. The system obtains underlined facial data through a video dataset or from the real-time environment. Subsequently, face/foreground and background keyframes are extracted from at hand captured video data. Finally, extracted facial image data is compared with the facial images in the database. In case no match is found with the existing data, a security alarm or signal is generated, alerting security personnel to take action. The proposed system is more accurate, has better performance, and is low cost compared with existing systems. INDEX TERMS Face recognition, HOG features, feedforward backpropagation neural network, surveillance video, principal component analysis.

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Section: Related Workmentioning

confidence: 99%

Real-Time Surveillance Through Face Recognition Using HOG and Feedforward Neural Networks

et al. 2019

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“…The algorithm compares only the grey frames after converting colour images from RGB [7], [8]. Jyothi and Vardhan suggest that motion detection algorithm applies principles of differencing frame where the algorithm compares pixels on how they change location after each frame [9]. A study conducted by Islam, Nahiyan and Kiber also agreed on the concept that considering frames of video as images, motion can be detected by performing absolute image difference [10], [11].…”

Section: Motion Detection Algorithmmentioning

confidence: 99%

Development of a portable community video surveillance system

Fen

2019

IJECE

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In 2016, a crime rate has been evidently increasing particularly in Kuala Lumpur areas, including reports on house break-ins, car thefts, motorcycle thefts and robbery. One way of deterring such cases is by installing CCTV monitoring system in premises such as houses or shops, but this usually requires expensive equipment and installation fees. In this paper a cheaper alternative of a portable community video surveillance system running on Raspberry Pi 3 utilizing OpenCV is presented. The system will detect motion based on image subtraction algorithm and immediately inform users when intruders are detected by sending a live video feed to a Telegram group chat, as well as sound the buzzer alarm on the Raspberry Pi. Additionally, any Telegram group members can request images and recorded videos from the system at any time by sending a get request in Telegram which will be handled by Telegram Bot. This system uses the Pi NoIR camera module as the image acquisition device equipped with a 36 LED infrared illuminator for night vision capability. In addition to the Python language, OpenCV, a computer vision simulation from Intel is also used for image processing tasks. The performance analysis of the completed system is also presented computational complexity while offering improved flexibility. The performance time is also presented, where the whole process is run with a noticeable 3 seconds delay in getting the final output.

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“…S.Naga Jyothi et al [29] have proposed the real time security surveillance system using IoT. The system design uses Motion Detection algorithm written in Python as a default programming environment.…”

Section: Security Based Surveillance Systemmentioning

confidence: 99%

CCTV Surveillance System, Attacks and Design Goals

Muthusenthil¹,

Kim²

2018

IJECE

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Closed Circuit Tele-Vision surveillance systems are frequently the subject of debate. Some parties seek to promote their benefits such as their use in criminal investigations and providing a feeling of safety to the public. They have also been on the receiving end of bad press when some consider intrusiveness has outweighed the benefits. The correct design and use of such systems is paramount to ensure a CCTV surveillance system meets the needs of the user, provides a tangible benefit and provides safety and security for the wider law-abiding public. In focusing on the normative aspects of CCTV, the paper raises questions concerning the efficiency of understanding contemporary forms of ‘social ordering practices’ primarily in terms of technical rationalities while neglecting other, more material and ideological processes involved in the construction of social order. In this paper, a 360-degree view presented on the assessment of the diverse CCTV video surveillance systems (VSS) of recent past and present in accordance with technology. Further, an attempt been made to compare different VSS with their operational strengths and their attacks. Finally, the paper concludes with a number of future research directions in the design and implementation of VSS.

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Design and implementation of real time security surveillance system using IoT

Cited by 41 publications

References 7 publications

Real-Time Surveillance Through Face Recognition Using HOG and Feedforward Neural Networks

Real-Time Surveillance Through Face Recognition Using HOG and Feedforward Neural Networks

Development of a portable community video surveillance system

CCTV Surveillance System, Attacks and Design Goals

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