Anyang Institute of Technology, Anyang, Henan 455000, China.
Comput Intell Neurosci. 2022 Mar 24;2022:2663834. doi: 10.1155/2022/2663834. eCollection 2022.
In sports, the essence of a complete technical action is a complete information structure pattern and the athlete's judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes' training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete's action recognition.
在体育运动中，完整技术动作的本质是完整的信息结构模式，而运动员对动作的判断实际上是对动作信息结构模式的识别。动作识别是指大脑根据不断变化的运动信息，识别和确认感知动作后，从其他动作中区分出来，并获得预测响应信息的能力。动作识别主要包括两个方面：一是基于视觉观察获取所需的动作信息，二是根据获取的动作信息判断动作，但这一过程的神经心理机制仍不清楚。本文针对内容复杂、场景多、动作丰富的体育视频，提出了一种基于聚类算法和多特征融合的新的关键帧提取方法。首先融合了多种特征，然后利用相似性度量来更完整、更全面地描述内容复杂的视频；其次，利用聚类算法根据场景对体育视频序列进行聚类，在场景多的情况下无需镜头检测分割的难度和复杂性；第三，根据最小运动标准提取关键帧，可以更准确地表示动作丰富的视频内容。同时，本文所使用的聚类算法进行了改进，提高了关键帧提取系统的离线计算效率。本文在分析经典卷积神经网络和循环神经网络算法在深度学习中优缺点的基础上，针对复杂场景、复杂动作和快速运动下的人体动作识别和分析，提出了一种改进的卷积网络和优化算法。实验表明，该算法在运动员训练执行和完成的识别方面达到了类似人类的观察效果。与其他算法相比，它对运动员的动作识别具有非常高的学习率和准确率。
