Volleyball Movement Object Detection and Behavior Recognition Method of Artificial Neural Network

Movement object detection method is the basis and key of the modem intelligent video surveillance system. It combines advanced technologies in many fields, such as artificial intelligence, image processing, and pattern recognition, and is the research area of computer vision technology. Therefore, i...

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Vydané v:Mobile information systems Ročník 2022; s. 1 - 10
Hlavní autori: Sun, Zhe, Zhang, Hongzhi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Hindawi 10.08.2022
John Wiley & Sons, Inc
Predmet:
Algorithms
Artificial intelligence
Artificial neural networks
Computer vision
Image processing
Information processing
Motion perception
Neural networks
Object recognition
Pattern recognition
Simulation
Surveillance systems
Target detection
Target recognition
Volleyball
China
ISSN:1574-017X, 1875-905X
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Abstract Movement object detection method is the basis and key of the modem intelligent video surveillance system. It combines advanced technologies in many fields, such as artificial intelligence, image processing, and pattern recognition, and is the research area of computer vision technology. Therefore, it is important to explore the motion detection and target recognition algorithms. The data collection method has concluded that T > 60 is absent from four different videos. The T value is traversed from 0–255 to select the largest T value as the split threshold. Through the analysis, the range of the threshold selection can be reduced to 0-60, thus improving the operational efficiency.
AbstractList Movement object detection method is the basis and key of the modem intelligent video surveillance system. It combines advanced technologies in many fields, such as artificial intelligence, image processing, and pattern recognition, and is the research area of computer vision technology. Therefore, it is important to explore the motion detection and target recognition algorithms. The data collection method has concluded that T > 60 is absent from four different videos. The T value is traversed from 0–255 to select the largest T value as the split threshold. Through the analysis, the range of the threshold selection can be reduced to 0-60, thus improving the operational efficiency.
Author Sun, Zhe
Zhang, Hongzhi
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Copyright Copyright © 2022 Zhe Sun and Hongzhi Zhang.
Copyright © 2022 Zhe Sun and Hongzhi Zhang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms Algorithms
Artificial intelligence
Artificial neural networks
Computer vision
Image processing
Information processing
Motion perception
Neural networks
Object recognition
Pattern recognition
Simulation
Surveillance systems
Target detection
Target recognition
Volleyball
Title Volleyball Movement Object Detection and Behavior Recognition Method of Artificial Neural Network
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