Image Energy Saving Recognition Technology of Monitoring System Based on Ant Colony Algorithm

In order to solve the shortcomings of traditional monitoring and alarm system, the key technology of intelligent video monitoring system-image processing and recognition technology is studied. The image energy-saving recognition technology of monitoring system based on ant colony algorithm is propos...

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Vydané v:Wireless communications and mobile computing Ročník 2022; číslo 1
Hlavný autor: Bao, Linxia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Hindawi 2022
John Wiley & Sons, Inc
Predmet:
Alarm systems
Algorithms
Ant colony optimization
Artificial intelligence
Cameras
Closed circuit television
Digital video
Energy conservation
False alarms
Human error
Image processing
Image transmission
Invariants
Monitoring
Monitoring systems
Multimedia
Neural networks
Object recognition
R&D
Recording equipment
Research & development
Sensors
Surveillance
Target recognition
Video recorders
China
ISSN:1530-8669, 1530-8677
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Abstract In order to solve the shortcomings of traditional monitoring and alarm system, the key technology of intelligent video monitoring system-image processing and recognition technology is studied. The image energy-saving recognition technology of monitoring system based on ant colony algorithm is proposed. The ant colony algorithm is used to segment the image, extract the suspicious area, and then deal with the suspicious area separately by using the reasoning based on default rules in artificial intelligence. Using the trained neural network, the recognition experiment is carried out on 40 samples, and the recognition accuracy is more than 93%, which shows that it is very effective to use 7 invariant moments in the target image area as the characteristic parameters of target recognition. The microcomputer 2 renewal for a single target area is identified by using a moment invariant calculation force method and neural network. The execution time of the test on the microcomputer of Saijie 1.0 is microsecond. The experiment proves that it is practical and reliable, meets the real-time requirements, and can transmit the alarm signal and suspicious area image to the alarm center through the network.
AbstractList In order to solve the shortcomings of traditional monitoring and alarm system, the key technology of intelligent video monitoring system-image processing and recognition technology is studied. The image energy-saving recognition technology of monitoring system based on ant colony algorithm is proposed. The ant colony algorithm is used to segment the image, extract the suspicious area, and then deal with the suspicious area separately by using the reasoning based on default rules in artificial intelligence. Using the trained neural network, the recognition experiment is carried out on 40 samples, and the recognition accuracy is more than 93%, which shows that it is very effective to use 7 invariant moments in the target image area as the characteristic parameters of target recognition. The microcomputer 2 renewal for a single target area is identified by using a moment invariant calculation force method and neural network. The execution time of the test on the microcomputer of Saijie 1.0 is microsecond. The experiment proves that it is practical and reliable, meets the real-time requirements, and can transmit the alarm signal and suspicious area image to the alarm center through the network.
Author Bao, Linxia
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10.1109/ACCESS.2020.3004178
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ContentType Journal Article
Copyright Copyright © 2022 Linxia Bao.
Copyright © 2022 Linxia Bao. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: Copyright © 2022 Linxia Bao.
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In order to solve the shortcomings of traditional monitoring and alarm system, the key technology of intelligent video monitoring system‐image processing and...
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SubjectTerms Alarm systems
Algorithms
Ant colony optimization
Artificial intelligence
Cameras
Closed circuit television
Digital video
Energy conservation
False alarms
Human error
Image processing
Image transmission
Invariants
Monitoring
Monitoring systems
Multimedia
Neural networks
Object recognition
R&D
Recording equipment
Research & development
Sensors
Surveillance
Target recognition
Video recorders
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