Object Extraction of Tennis Video Based on Deep Learning

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquire...

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Veröffentlicht in:Wireless communications and mobile computing Jg. 2022; H. 1
1. Verfasser: Huang, Huadong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Hindawi 2022
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Automatic control
Computer peripherals
Computer vision
Deep learning
Digital imaging
Image acquisition
Image processing
Infrared detectors
Infrared imagery
Machine learning
Morphology
Moving targets
Neural networks
Object recognition
Pattern recognition
Shape
Signal processing
Target detection
Target recognition
Technical services
Tennis
Tracking
Wireless networks
ISSN:1530-8669, 1530-8677
Online-Zugang:Volltext
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Zusammenfassung:Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/5402410