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Sports image detection based on particle swarm optimization algorithm

机译：基于粒子群优化算法的体育图像检测

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Athletes can still insist on the practicality of sports practice in today's sports environment by locating image detection technology, video and video files, as well as broadcast data detection key format. However, from the most advanced of the situation in the country, goal can confirm that the light detection dong area is still immature. So let's take a look at the video cloth applied to detect motion pictures in ages to search for software. Method are looking at the side of detection, grayscale processing, grasping the project, target identification, and see the light detection era. The actual sports programs and the desire to meet the different needs of the sports image detection mix. At the same time, in the present study, found that the popularity of athletes and judge, motion recognition of motor behavior. Along with affirmed the effectiveness of this method of research, have created a platform for inspection. Results show that the research method is always practical, can provide a theoretical basis for future research. A set of rules contained in a hierarchy: First, the Particle Swarm Optimize [PSO] algorithm is used to the annotation image per day. In the experiment, 3 sets of data were used to evaluate a set of rules, the results show that the performance of the algorithm.
机译：运动员仍然可以通过定位图像检测技术，视频和视频文件以及广播数据检测键格式来坚持当今运动环境的实用性。然而，从国家的最先进的情况来看，目标可以确认光检测董区仍然不成熟。因此，让我们来看看应用于检测运动图片的视频布，以搜索软件。方法正在观察检测，灰度处理，掌握项目，目标识别，看光检测时代的侧面。实际的体育计划和满足体育图像检测混合的不同需求的愿望。与此同时，在本研究中，发现运动员和判断的普及，运动行为的运动识别。随着这种研究方法的有效性，已经创造了一个检查平台。结果表明，研究方法始终实用，可以为未来的研究提供理论依据。层次结构中包含的一组规则：首先，粒子群优化[PSO]算法用于每天注释图像。在实验中，使用3组数据来评估一组规则，结果表明该算法的性能。

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