Automatic detection technology of sports athletes based on image recognition technology
Abstract In order to improve the motion recognition effect of sports athletes based on image recognition technology, this study takes the current common diving athletes as the research material in the actual research, and combines the research status of image recognition to study the athlete’s motio...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2019-01-01
|
Series: | EURASIP Journal on Image and Video Processing |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13640-019-0415-x |
id |
doaj-aae6715cfe384b8e8df3ec7ab64c65e4 |
---|---|
record_format |
Article |
spelling |
doaj-aae6715cfe384b8e8df3ec7ab64c65e42020-11-25T01:31:26ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812019-01-01201911910.1186/s13640-019-0415-xAutomatic detection technology of sports athletes based on image recognition technologyGuangjing Li0Cuiping Zhang1Department of Physical Education, Tianjin University of CommerceTianjin Huaxin Zhiyuan Technology Co., Ltd.Abstract In order to improve the motion recognition effect of sports athletes based on image recognition technology, this study takes the current common diving athletes as the research material in the actual research, and combines the research status of image recognition to study the athlete’s motion recognition from image processing. Simultaneously, in this study, the gradient segmentation method is used to segment the image, the research object is segmented from the video image, the traditional image grayscale method is improved, and the image segmentation algorithm adapted to the diving motion is obtained. On this basis, this study combines Gaussian mixture background modeling and background subtraction to achieve the detection and extraction of target human body regions, and uses morphological operators to deal with noise and void phenomena in foreground images. The example analysis shows that the proposed method has certain practicality and can provide theoretical reference for subsequent related research.http://link.springer.com/article/10.1186/s13640-019-0415-xImage recognitionSportsAthletesDetectionVideo image |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangjing Li Cuiping Zhang |
spellingShingle |
Guangjing Li Cuiping Zhang Automatic detection technology of sports athletes based on image recognition technology EURASIP Journal on Image and Video Processing Image recognition Sports Athletes Detection Video image |
author_facet |
Guangjing Li Cuiping Zhang |
author_sort |
Guangjing Li |
title |
Automatic detection technology of sports athletes based on image recognition technology |
title_short |
Automatic detection technology of sports athletes based on image recognition technology |
title_full |
Automatic detection technology of sports athletes based on image recognition technology |
title_fullStr |
Automatic detection technology of sports athletes based on image recognition technology |
title_full_unstemmed |
Automatic detection technology of sports athletes based on image recognition technology |
title_sort |
automatic detection technology of sports athletes based on image recognition technology |
publisher |
SpringerOpen |
series |
EURASIP Journal on Image and Video Processing |
issn |
1687-5281 |
publishDate |
2019-01-01 |
description |
Abstract In order to improve the motion recognition effect of sports athletes based on image recognition technology, this study takes the current common diving athletes as the research material in the actual research, and combines the research status of image recognition to study the athlete’s motion recognition from image processing. Simultaneously, in this study, the gradient segmentation method is used to segment the image, the research object is segmented from the video image, the traditional image grayscale method is improved, and the image segmentation algorithm adapted to the diving motion is obtained. On this basis, this study combines Gaussian mixture background modeling and background subtraction to achieve the detection and extraction of target human body regions, and uses morphological operators to deal with noise and void phenomena in foreground images. The example analysis shows that the proposed method has certain practicality and can provide theoretical reference for subsequent related research. |
topic |
Image recognition Sports Athletes Detection Video image |
url |
http://link.springer.com/article/10.1186/s13640-019-0415-x |
work_keys_str_mv |
AT guangjingli automaticdetectiontechnologyofsportsathletesbasedonimagerecognitiontechnology AT cuipingzhang automaticdetectiontechnologyofsportsathletesbasedonimagerecognitiontechnology |
_version_ |
1725086680302485504 |