SAFE Model Approach to Construction of Intelligent Security Systems

2005 ◽  
Author(s):  
Z. Chaczko ◽  
S.N. Sinha
Keyword(s):  
Security Systems ◽  
Intelligent Security ◽  
Safe Model ◽  
Model Approach

Intelligent security systems

1986 ◽  
Vol 4 (4) ◽  
pp. 328-331
Author(s):  
Gerry Stapley
Keyword(s):  
Security Systems ◽  
Intelligent Security

scholarly journals A SIMULATION MODEL FOR MANAGING PARAMETERS OF INTELLIGENT SECURITY SYSTEM

2020 ◽  
pp. 3-5
Author(s):  
Anzor Babunashvili ◽  
Nona Kukhianidze
Keyword(s):  
Simulation Model ◽  
Video Surveillance ◽  
Large Distance ◽  
Security Management ◽  
Security System ◽  
Management Systems ◽  
Surveillance Systems ◽  
Security Systems ◽  
Control Devices ◽  
Intelligent Security

Security systems are the guarantee of stable functioning of the organization, company, office, industry, housing and other types of buildings. For entire security, complex control devices are set on the building which has to be protected, like fire and burglar alarms, video-audio controls and access systems. Intelligent Security Management System is built on Atmel’s microcontrollers. This allows the system to be flexible and to modify it easily if needed. The reliability of the security system depends on its uniqueness. This given security system is easily modified and hence its reliability is high. The system can be easily integrated into a variety of external modules, which can further increase the factor of reliability. It’s also easy to adopt it to various security management systems. Many of the security and video surveillance systems allow to control the situation of the object from a large distance. From any point of the planet, we can track and manage the security system modes and make easy changes in them, which enable adequate reaction.Digital complexes of video surveillance are one of the variants of defense organization. The main areas of their use are the most important objects and large areas where buildings are located far from each other.

scholarly journals Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6745
Author(s):  
Wen-Li Zhang ◽  
Kun Yang ◽  
Yi-Tao Xin ◽  
Ting-Song Zhao
Keyword(s):  
Object Tracking ◽  
Human Error ◽  
Tracking Algorithm ◽  
Movement Trajectory ◽  
Shopping Malls ◽  
Security Systems ◽  
Depth Images ◽  
Combined Features ◽  
Intelligent Security ◽  
Siamese Networks

Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.

Overview of Biometrics and Biometrics Systems

2013 ◽  
pp. 6-25 ◽  
Keyword(s):  
System Performance ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Security Systems ◽  
Biometric Data ◽  
Multimodal Biometric System ◽  
System Components ◽  
Intelligent Information ◽  
And Performance ◽  
Intelligent Security

Recent security threats increase the necessity to establish the identity of every person. Biometric authentication is a solution to person authentication by analyzing physiological or behavioral characteristics. In this chapter, various biometric notions and terms are reviewed, along with typical biometric system components and different functionalities and performance parameters. The design and development of a biometric system, depending on a particular application scenario, is covered. This chapter also focuses on the inherent issues associated with biometric data and system performance through introducing radically new methods based on intelligent information fusion and intelligent pattern recognition, thus creating a notion of intelligent security systems. At the end of the chapter, the potential drawbacks of biometric unimodal systems, which serves as the motivation to introduce the concept of multimodal biometric system in the context of intelligent security systems, is discussed.

scholarly journals Fuzzy Face Recognition Based On Skin Texture Fusion Model

2019 ◽  
Vol 8 (6) ◽  
pp. 541-544
Keyword(s):  
Recognition Rate ◽  
Fuzzy Model ◽  
Fusion Model ◽  
Security Systems ◽  
Skin Texture ◽  
Human Machine Interaction ◽  
Face Model ◽  
Test Face ◽  
Machine Interaction ◽  
Model Approach

Facerecognition is a research are in computer vision and pattern recognition because of its importance in real applications like human machine interaction, video surveillance, and security systems. Here we have proposed a fuzzy model for robust facerecognition using gradient and texture information. Initially, the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature of face skin from the original images are extracted. These two features are used for the development of our fuzzy model. For the analysis of faces, a content-based similarity measure is developed and used for data analysis of trained face model and test face model. The proposed algorithm is experimented on LFW, AR, and ORL face databases. The proposed fuzzy face fusion model approach shows that our proposed method is having good recognition rate compared to facerecognition methods developed recently.

scholarly journals Tools for development of neural components of intelligent security systems

2014 ◽  
Vol 3 (26) ◽  
pp. 7
Author(s):  
Filipp Gennadyevich Nesteruk ◽  
Igor Vitalievich Kotenko
Keyword(s):  
Security Systems ◽  
Intelligent Security

scholarly journals Intelligent Security Monitoring System Based on RISC-V SoC

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1366
Author(s):  
Wenjuan Wu ◽  
Dongchu Su ◽  
Bo Yuan ◽  
Yong Li
Keyword(s):  
Human Resources ◽  
Monitoring System ◽  
Processing Speed ◽  
Collaborative Design ◽  
Moving Objects ◽  
Security Systems ◽  
Security Monitoring ◽  
System A ◽  
Security Monitoring System ◽  
Intelligent Security

With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance.

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