scholarly journals DETECTION AND RECOGNITION OF MOVING OBJECTS FOR PHYSICAL SYSTEMS PROTECTION IN NUCLEAR FACILITIES

2021 ◽  
Vol 40 (2) ◽  
pp. 85-98
Author(s):  
Mohamed Gaber ◽  
Ashraf A. M. Khalaf ◽  
Imbaby Mahmoud ◽  
Mohamed El-Tokhy
Keyword(s):  
Moving Objects ◽  
Nuclear Facilities ◽  
Physical Systems ◽  
Detection And Recognition

A model of detecting cyberattacks on cyber-physical systems of critical infrastructure nuclear facilities

2021 ◽  
pp. 128-133
Author(s):  
A.A. Chechulin ◽  
V.O. Popova
Keyword(s):  
Critical Infrastructure ◽  
Cyber Physical Systems ◽  
Nuclear Facilities ◽  
Physical Systems

The article presents an approach to classification that will help to identify specific areas of possible cyberattacks and significantly narrow the range of targets vulnerable to attackers. Criteria are considered that allow one to classify cyber-physical systems (attack targets) that are part of nuclear facilities. It is proposed to enter into the basis of the classification such concepts as an object, an attacking and attacking action. The novelty in this work lies in the development of a classification that can be used most effectively to assess the vulnerabilities of systems of critical infrastructure facilities.

scholarly journals Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1373 ◽  
Author(s):  
Wahyu Rahmaniar ◽  
Wen-June Wang ◽  
Hsiang-Chieh Chen
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Moving Objects ◽  
Region Of Interest ◽  
Computation Time ◽  
Important Application ◽  
Feature Points ◽  
Aerial Surveillance ◽  
Precision Rate ◽  
Detection And Recognition

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.

Application of a model for detecting cyberattacks on cyber-physical systems of critical infrastructure nuclear facilities

2021 ◽  
Vol 4 ◽  
pp. 60-65
Author(s):  
V.O. Popov ◽  
◽  
A.A. Chechulin
Keyword(s):  
Critical Infrastructure ◽  
Cyber Physical Systems ◽  
Nuclear Facilities ◽  
Generalized Model ◽  
Physical Systems ◽  
Proposed Model

In this part of the article, the application of the approach to the classification proposed in the first part is presented. As a result of the application of the proposed classification using the generalized model, it is supposed to determine the attack vector within the framework of the proposed model and draw conclusions on the main areas of cyber-physical systems that are vulnerable to attacks by malefactors.

scholarly journals Detection and Recognition of Abnormal Running Behavior in Surveillance Video

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ying-Ying Zhu ◽  
Yan-Yan Zhu ◽  
Wen Zhen-Kun ◽  
Wen-Sheng Chen ◽  
Qiang Huang
Keyword(s):  
Moving Objects ◽  
Tracking Algorithm ◽  
Multitarget Tracking ◽  
Surveillance Video ◽  
Criminal Cases ◽  
Time Performance ◽  
Intersection Area ◽  
Spatiotemporal Parameters ◽  
Abnormal Behaviors ◽  
Detection And Recognition

Abnormal running behavior frequently happen in robbery cases and other criminal cases. In order to identity these abnormal behaviors a method to detect and recognize abnormal running behavior, is presented based on spatiotemporal parameters. Meanwhile, to obtain more accurate spatiotemporal parameters and improve the real-time performance of the algorithm, a multitarget tracking algorithm, based on the intersection area among the minimum enclosing rectangle of the moving objects, is presented. The algorithm can judge and exclude effectively the intersection of multitarget and the interference, which makes the tracking algorithm more accurate and of better robustness. Experimental results show that the combination of these two algorithms can detect and recognize effectively the abnormal running behavior in surveillance videos.

scholarly journals Through the Wall, Recognize Moving Targets Based on Micro-Doppler Signatures

2018 ◽  
Vol 8 (6) ◽  
pp. 5227
Author(s):  
Thamir Rashed Saeed ◽  
Mahmuod Hamza Al-Muifraje ◽  
Ghufran M. Hatem
Keyword(s):  
Mathematical Model ◽  
Moving Objects ◽  
Doppler Frequency ◽  
Human Movement ◽  
Doppler Frequency Shift ◽  
Human Walking ◽  
Attractive Feature ◽  
Moving Targets ◽  
Human Movements ◽  
Detection And Recognition

Radar is a promising device for detection and recognition of invisible moving objects, Where, the micro-Doppler frequency shift caused by moving the object's parts have been represented as an attractive feature in the recognition process. In spite of that, no thorough analysis of human movement by bicycle and no discriminate from the running one through the wall in the literature. This paper presents a mathematical model of bicycle movement, then, the recognition of moving objects through the wall. Where three human movements; walking, running and on a bicycle have been recognized through two types of wall wood and cork. The theoretical analysis and measured was given a recognition 98.7% for human walking on his feet, 99% of the passenger on a bicycle, and 98% of the person running have been achieved without walls. While, a 95.4%, 96.2%, and 95% recognition have been gained from walking, moving by bicycle and running with wooden wall and 94%, 94.8%, and 93.3% respectively with cork wall. 2.4 GHz as a detector and SVM as classifier are used.

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