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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Research on athlete training behavior based on improved support vector algorithm and target image detection
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Research on athlete training behavior based on improved support vector algorithm and target image detection

机译：基于改进的支持向量算法和目标图像检测的运动员训练行为研究

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摘要

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.
机译：鉴于传统目标检测和跟踪算法的缺陷和缺陷在准确地检测不同场景中的目标和目标中，基于目前的目标检测和国内外跟踪技术水平，本文提出了目标检测使用神经网络算法的算法和跟踪方法，将其应用于运动员训练模型。基于Alex-Net网络结构，本文设计了三层卷积层和两层完全连接层。最后一层用作SVM分类器的输入，并且目标分类结果由SVM分类器获得。另外，本文在卷积层和完全连接的层之间添加SPP层，使得能够在用于不同大小的输入图像的完全连接层之前获得的特征图的相同尺寸。研究结果表明，该方法具有一定的识别效果，可应用于运动员培训。

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