基于改进密集轨迹算法的网球辅助教学评价技术研究

Q4 Decision Sciences
Mengran Niu
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引用次数: 0

摘要

随着人工智能技术的不断发展,网球机器人开始进入人们的生活。本研究提出一种基于改进密集轨迹算法的网球辅助教学评价方法。结果表明,当非定长轨迹的分割参数nσ和nΓ分别为2和3时,采用分割方法的识别效果最好。切比雪夫距离公式得到的计算效果较好,各类动作之间的距离主要分布在0.2左右,它们与标准动作之间实际存在的距离较小。4K视频的BD率从11.45%提高到32.98%,8K视频的BD率从1.82%提高到7.61%。系统分割的贴图数量越多,性能损失就越大,总体而言,优化后的MCTS对编码性能的影响是可以接受的。从测试的全局角度来看,仍然可以接受更快的码流融合给系统带来的传输延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on tennis-assisted teaching assessment technology based on improved dense trajectory algorithm
With the continuous development of artificial intelligence technology, tennis robots begin to enter people's lives. This study proposes a tennis-assisted teaching evaluation method based on improved dense trajectory algorithm. The results show that the best recognition effect is obtained under the division method when the division parameters nσ and nΓ of non-fixed-length trajectories are 2 and 3 respectively. The calculation effect obtained by Chebyshev distance formula is better, and the distance between various types of actions is mainly distributed around 0.2, and the actual distance existing between them and the standard action is smaller. The BD rate of 4K video increases from 11.45% to 32.98%, while that of 8K video increases from 1.82% to 7.61%. The more the number of tile divided by the system, the more the performance is lost, and globally, the coding performance affected by the optimised MCTS is acceptable. From the global view of the test, it is still possible to accept the transmission delay caused by the faster code stream fusion to the system.
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来源期刊
International Journal of Networking and Virtual Organisations
International Journal of Networking and Virtual Organisations Decision Sciences-Information Systems and Management
CiteScore
1.40
自引率
0.00%
发文量
25
期刊介绍: IJNVO is a forum aimed at providing an authoritative refereed source of information in the field of Networking and Virtual Organisations.
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