Volleyball Video Moving Target Tracking and Detection Algorithm Based on Multisensor Information Fusion

With the improvement of the level of sports competition technology, pure sports game broadcasting can no longer meet the requirements of various users. Users urgently need new technologies to meet the needs of quickly obtaining game information. The 5G Internet of Things is rapidly rising; volleybal...

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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2022; no. 1
Main Authors: Wang, Cong, Dong, Kongmei
Format: Journal Article
Language:English
Published: Oxford Hindawi 2022
John Wiley & Sons, Inc
Subjects:
Accuracy
Algorithms
Artificial intelligence
Communication
Data integration
Datasets
Feedback
Hypotheses
Internet of Things
Moving targets
Multisensor fusion
Neural networks
Neurosciences
New technology
Probabilistic models
Segmentation
Sports
Target detection
Tracking
User requirements
Volleyball
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary:With the improvement of the level of sports competition technology, pure sports game broadcasting can no longer meet the requirements of various users. Users urgently need new technologies to meet the needs of quickly obtaining game information. The 5G Internet of Things is rapidly rising; volleyball is also gradually into people’s life. To solve the above problems, the research on the volleyball video moving target tracking and detection algorithm based on 5G Internet of Things communication and artificial intelligence becomes very important. This article is aimed at comparing the tracking and detection algorithms of volleyball video moving targets through 5G Internet of Things communication and artificial intelligence. In the process of detecting moving targets, several commonly used background detection methods are comprehensively compared and analyzed, and a method based on adaptive background difference is proposed. The algorithm uses Gaussian mixture model for background modeling, uses the OTSU threshold algorithm to determine the segmentation threshold T, and proposes a background update method that combines the expected full statistics and the L-close window to accelerate the background update speed, so that a more accurate sports prospect target. Experimental results show that the algorithm can detect and track moving targets in volleyball videos well in a fixed scene or when moving targets are not blocked, with a detection accuracy of more than 92%.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2022/8948431