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Research on sports retrieval recognition of action based on feature extraction and SVM classification algorithm

机译：基于特征提取和SVM分类算法的运动检索识别运动检索识别研究

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The feature extraction speed of the traditional athlete motion retrieval algorithm is slow, and it often takes dozens of minutes or even hours to analyze a video. The speed of this feature extraction obviously cannot meet the needs of big data video analysis. In response to these two problems exposed by Action Bank under large-scale data, this paper proposes to apply the template learning method based on spectral clustering to Action Bank, which replaces the cumbersome manual selection template step and is easy to generalize to different databases. Moreover, in view of the disadvantage of slow speed of extracting Action Bank features, this paper proposes a fast algorithm for accumulating Action Bank. In addition, this study uses the lookup table method instead of the time-consuming steps of the correlation distance calculation in template matching, which greatly accelerates the time of feature extraction. Finally, this study design experiments to analyze the performance of the algorithm. Through research, it can be seen that the algorithm of this study can be applied to athletes' sports retrieval and has certain recognition effects.
机译：传统运动员运动检索算法的特征提取速度速度慢，通常需要几十分钟甚至几小时才能分析视频。此特征提取的速度显然无法满足大数据视频分析的需求。响应于在大规模数据下由行动组公开的这两个问题，本文提出基于频谱聚类的模板学习方法应用于操作组，该方法替换了繁琐的手动选择模板步骤，易于概括到不同的数据库。此外，鉴于提取动作银行特征的缓慢速度的缺点，本文提出了一种累积动作组的快速算法。此外，本研究使用查找表方法而不是模板匹配中相关距离计算的耗时步骤，这大大加速了特征提取的时间。最后，这项研究设计实验来分析算法的性能。通过研究，可以看出该研究的算法可以应用于运动员的运动检索，并具有一定的识别效果。

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