Intelligent surveillance system used one new method of image recognition

2011 ◽  
Author(s):  
Gu Lingkang ◽  
Zhou Mingzheng
Keyword(s):  
Image Recognition ◽  
Surveillance System ◽  
New Method ◽  
Intelligent Surveillance

Situational Strategic Awareness Monitoring Surveillance System–Microcomputer and Microcomputer Clustering used for Intelligent, Economical, Scalable, and Deployable Approach for Safeguarding Materials

2020 ◽  
Vol 2020 (3) ◽  
pp. 60408-1-60408-10
Author(s):  
Kenly Maldonado ◽  
Steve Simske
Keyword(s):  
Surveillance System ◽  
Cost Effective ◽  
Government Agencies ◽  
Electronic Systems ◽  
Time Data ◽  
Potential Threat ◽  
Intelligent Surveillance ◽  
World Markets ◽  
Programming Software ◽  
And Control

The principal objective of this research is to create a system that is quickly deployable, scalable, adaptable, and intelligent and provides cost-effective surveillance, both locally and globally. The intelligent surveillance system should be capable of rapid implementation to track (monitor) sensitive materials, i.e., radioactive or weapons stockpiles and person(s) within rooms, buildings, and/or areas in order to predict potential incidents proactively (versus reactively) through intelligence, locally and globally. The system will incorporate a combination of electronic systems that include commercial and modifiable off-the-shelf microcomputers to create a microcomputer cluster which acts as a mini supercomputer which leverages real-time data feed if a potential threat is present. Through programming, software, and intelligence (artificial intelligence, machine learning, and neural networks), the system should be capable of monitoring, tracking, and warning (communicating) the system observer operations (command and control) within a few minutes when sensitive materials are at potential risk for loss. The potential customer is government agencies looking to control sensitive materials and/or items in developing world markets intelligently, economically, and quickly.

scholarly journals Design of Intelligent Surveillance System for Cable Connector

2020 ◽  
Vol 598 ◽  
pp. 012096
Author(s):  
Hongguang Li ◽  
Xinhua Feng ◽  
Deming Wu ◽  
Lifeng Liang
Keyword(s):  
Surveillance System ◽  
Intelligent Surveillance

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

scholarly journals A method for separating O-wave and X-wave and its application in digital ionosonde

2014 ◽  
Vol 56 (5) ◽  
Author(s):  
Wang Shun ◽  
Chen Ziwei ◽  
Zhang Feng ◽  
Gong Zhaoqian ◽  
Li Jutao ◽  
...  
Keyword(s):  
Image Recognition ◽  
Initial Phase ◽  
Phase Shifters ◽  
New Method ◽  
Digital Method ◽  
Electrical Method ◽  
Circularly Polarized ◽  
Automatic Scaling ◽  
Polarized Waves ◽  
Phase 0

<p><strong><em></em></strong>Separation for O wave and X wave is a very important job in interpretation of ionograms, which is premise for automatic scaling. In this paper, a new digital method for separating O wave and X wave is presented, based on a numerical synthesizing technique, which is different from using image recognition to separate trace O and trace X in the ionograms, and from using the electrical method to synthesize and detect circularly polarized waves. By replacing analog phase shifters and switches in existing ionosonde with digital phase shifters with different initial phase, 0°, +90°, −90°, circularly polarized waves are synthesized digitally within the range of 1-30 MHz, which eliminates the nonlinearity and expands the bandwidth of the ionosonde, and there is no need to switch the analog switches continuously. The new method has been successfully applied to CAS-DIS ionosonde and testing results show that the new digital method is capable of separating O wave and X wave well.</p>

Identification of crowd behaviour patterns using stability analysis

2021 ◽  
pp. 1-15
Author(s):  
V. Muhammed Anees ◽  
G. Santhosh Kumar
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Security Requirements ◽  
Video Sequences ◽  
World Population ◽  
System Modelling ◽  
Intelligent Surveillance ◽  
Flow Modelling ◽  
Wide Range ◽  
Crowd Behaviour

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.

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