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云计算图像处理在运动员训练高分辨率图像检测中的应用。

Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection.

机构信息

Physical Education College of Xiangnan University, Chenzhou, Hunan 423000, China.

出版信息

Comput Intell Neurosci. 2022 Oct 3;2022:7423411. doi: 10.1155/2022/7423411. eCollection 2022.

Abstract

The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, which are often applied to various fields, and some other aspects of information communication and processing services. It has been used on various mobile phone, computer, or tablet user clients. Terminal equipment is subject to the ultralow latency and low energy consumption requirements of the above-mentioned applications. Therefore, the gap between resource-demanding application services and resource-limited mobile devices will bring great problems to the current and future development of IoT mobile applications. Based on the local image features of depth images, this paper designs an image detection method for athletes' motion posture. First, according to the characteristics of the local image, the depth image of the athlete obtained through Kinect is converted into bone point data. Next, a 3-stage exploration algorithm is used to perform block matching calculations on the athlete's bone point image to predict the athlete's movement posture. At the same time, using the characteristics of the Euclidean distance of the bone point image, the movement behavior is recognized. According to the experimental results, for some external environmental factors, such as sun illumination and other factors, the image detection method designed in this paper can effectively avoid their interference and influence and show the movement posture of athletes, showing excellent accuracy and robustness in predicting the movement posture of athletes and action recognition. This method can simplify a series of calibration tasks in the initial stage of 3D video surveillance and infer the posture of the observation target and recognize it in real time. The one that has good application values has specific reference values for the same job.

摘要

物联网移动应用技术和人工智能技术的飞速发展,催生了许多能够满足现代生活需求的服务,例如增强现实技术、人脸识别服务以及语言识别和翻译等,这些技术经常应用于各个领域,以及其他一些信息通信和处理服务方面。它已经在各种移动电话、计算机或平板电脑用户客户端上使用。终端设备受到上述应用超低延迟和低能耗要求的限制,因此,资源密集型应用服务与资源有限的移动设备之间的差距将给物联网移动应用的当前和未来发展带来巨大问题。本文基于深度图像的局部图像特征,设计了一种运动员运动姿势的图像检测方法。首先,根据局部图像的特征,将通过 Kinect 获取的运动员深度图像转换为骨骼点数据。接下来,使用 3 阶段探索算法对运动员的骨骼点图像进行块匹配计算,以预测运动员的运动姿势。同时,利用骨骼点图像的欧几里得距离特征,识别运动行为。根据实验结果,对于一些外部环境因素,如太阳光照等因素,本文设计的图像检测方法可以有效地避免它们的干扰和影响,并显示运动员的运动姿势,在预测运动员的运动姿势和动作识别方面表现出出色的准确性和鲁棒性。该方法可以简化 3D 视频监控初始阶段的一系列校准任务,并实时推断观察目标的姿势并对其进行识别。具有良好应用价值的方法对同类工作具有具体的参考价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b6a/9550408/309113b4aa43/CIN2022-7423411.001.jpg

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